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IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Universidade da Coruna
Automatic Pixel-Parallel Extraction of the Retinal VascularTree: Algorithm Design, On-Chip Implementation and
Applications
Carmen Alonso Montes
July 18th, 2008
Supervisors: Manuel Gonzalez Penedo and David Lopez Vilarino
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Motivation
Retinal vessel treeMedical research
early diagnosispatient monitoring
Biometric researchRetinal vessel patternAuthentication applications
Bottleneck : High computation effort required for the extraction of the retinalvessel treeGoals of this thesis:
Design of an algorithm to extract the retinal vessel tree at a high computation speedPixel-parallel approachCustomization of the algorithm and tuning the main parameters for the retinal vessel treeextraction taskAnalysis of the reliability (accuracy) and time performanceAn implementable algorithm into a processor array with SIMD processing capabilitiesIntegration of the algorithm into practical applications
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Medical images
Human eye cross-sectional view Non-Mydriatic Canon CR6-45NM
Fundus retinal images
Cameras capture ultra high-resolution digital images
Types:
MydriaticNon-mydriatic
Non-mydriatic are the most commonly used since it is not necessary to use dilation drops
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Types of digital retinal images
Digital fluorescing angiography
Dying trace method
High risk of adverseeffects on the patient
The most intrusivetechnique
Colour fundus photography
Uses a white zenon flashlight
Usually in RGB format,and channel G is selectedto get the gray scaleimage
Red free photography
Invisible infrared light toilluminate the retina
The patient does notexperience blinding whitelight during this process
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Types of digital retinal images
Digital fluorescing angiography
Dying trace method
High risk of adverseeffects on the patient
The most intrusivetechnique
Colour fundus photography
Uses a white zenon flashlight
Usually in RGB format,and channel G is selectedto get the gray scaleimage
Red free photography
Invisible infrared light toilluminate the retina
The patient does notexperience blinding whitelight during this process
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Types of digital retinal images
Digital fluorescing angiography
Dying trace method
High risk of adverseeffects on the patient
The most intrusivetechnique
Colour fundus photography
Uses a white zenon flashlight
Usually in RGB format,and channel G is selectedto get the gray scaleimage
Red free photography
Invisible infrared light toilluminate the retina
The patient does notexperience blinding whitelight during this process
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Image features
Several vessel widths
Vessels are usually low contrast, particularly narrow vessels
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Image features
A high variability of vessel widths
Vessels are usually low contrast, particularly narrow vessels
Variety of structures:
retina boundaryoptic diskpathologies
Central reflex which causes a complicated intensity cross-section
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
State of the art
Classification of retinal vessel tree extraction techniques:
Pattern Recognition , like matched filters, adaptive threshold, or region-based approaches
Model-based approaches, which include classical or geometric deformable models
Tracking-based approaches
Artificial intelligence-based approaches
Neural network approaches
Tube-like object detection approaches
Main drawbacks
High execution time, specially regarding real-time requirements
The definition and tuning of parameters is complex in some approaches
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
The most flexible tools to deal with the extraction of the retinal vessel tree are
Active contours
AdvantagesReasonably management of
NoiseAmbiguous boundaries
DrawbacksInteractive tool (not automatic)A strategy to compute the initial conditions must be definedHigh computation effort
SolutionCellular Active Contours
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Our proposal
Our proposal
Pixel Level Snakes (PLS)
Resolve the high computational cost of classic active contour techniquesBased on pixel level discretization of the the contoursMassively parallel computation on every contour cellImplemented on hardware architectures with SIMD capabilities (ACE4K, SCAMP-3 andspecific purpose integrated circuits)
Automatic computation of the initial conditions needed by PLS
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Medical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
Our proposal
Our proposal
Strategy: Fitting the exterior of the vessels
Robust control of the evolutionEasier initialisation (only 12.7% of pixels belong to vessels)
Implementable in a SIMD processor array
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Active contours: an overview
Elastic curveu(s) = (x(s), y(s)), s ∈ [0, 1]
Evolves from its initial shape and position as a result of the combined action ofExternal forces : Guide the contours towards the features of interestInternal forces : Control the smoothness of the contour
Main input imagesInitial contourExternal potential image (guiding information image)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS
Pixel Level Snakes
Three modules which interactdynamically:
Guiding InformationExtraction
information to guide theevolution
Contour Evolution
pixel-to-pixel shift of thecontours
Topological Transformation
SplittingMerging
Mathematical definition of the potential field
P(x, y) = kint Pint (x, y) + kext Pext (x, y)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS: evolution with external potential
External Potential
External Potential
External potential guides the contours towards the boundaries of interest
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS: evolution with internal potential
Internal Potential
Internal Potential
Internal potential maintains the smoothness of the contour
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS: evolution with balloon potential
Balloon Potential
Guiding forces with the balloon potential
Balloon potential moves the contours when the external potential is too weak
Final mathematical definition of the potential
P(x, y) = kint Pint (x, y) + kext Pext (x, y) + kinf Pinf (x, y)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Contour-based PLS
Contour-based PLS
Fully operative PLSimplementation
DCD module
controls the contourevolution
GFE module
computes the guidinginformation
IPE module
computes the internalpotential
Recursive low-passfilteringDiffusion operation
Evolution in the four cardinaldirections
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Contour-based PLS
Topological Transformations
Topological changes
Preservation of thetopologySplitting and merging
CPD module
Manages the topologicaloperations
Hole filling operation
High computation effort
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Region-based PLS
Region-based PLS
Boundaries of an active region
Eight movements in the four cardinaldirection are needed for a whole PLScycle
1 North-South-East-West2 Inversion of the regions3 North-South-East-West4 Inversion of the regions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
Region-based PLS
Topological transformations
No extra computation isneeded
No hole filling operation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS summary
Both proposals have been considered in the design of the algorithmContour based PLS
A robust control over the evolution is providedFaster evolution (4 movements in each PLS cycle)
Region based PLSThe topological changes are easily made
Drawbacks
More accurate external potential is needed
Apparently slower, since 8 movements are required in a PLS cycle
Advantages
Easily customizable for a particular task, if only inflation/deflation is needed
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Contour-based approachRegion-based approach
PLS summary
Both proposals have been considered in the design of the algorithmContour based PLS
A robust control over the evolution is providedFaster evolution (4 movements in each PLS cycle)
Region based PLSThe topological changes are easily made
Drawbacks
More accurate external potential is needed
Apparently slower, since 8 movements are required in a PLS cycle
Advantages
Easily customizable for a particular task, if only inflation/deflation is needed
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Goal
Automatic computation of the initial conditions from the original image
Tuning the parameters to fit the vessels
Calibration of the PLS parameters
An implementable HW version for a pixel parallel processor
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Approaches to solve the task
The first attempt consists on implementing the steps in terms of local operations
convolutionsarithmetic and logical operations
Cellular Neural Network (CNN) based implementation has been proposed
Straightforward methodology for image processing techniquesKey : improving the computation time provided by massively parallel processing
Problem : Some of the steps initially proposed cannot be implemented in a processor arraydue its complexity
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Conceptual definition of the stages
Stage 1 : Vessel pre-estimation
Determination of the vessel locationsPre-filtering steps to improve the signal-to-noise ratio
Stage 2 : Initial contour estimation
The initial conditions for PLS
Stage 3 : External potential estimation
Computation of the guiding information
Stage 4 : PLS evolution
Calibration of PLS
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 1: Vessel Pre-estimation (First approach)
Original image conditions
Non uniformity in the gray level values along the vessels
Ambiguity in the vessel boundaries
Noise
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 1: Vessel Pre-estimation (First approach)
Histogram Equalization
Goal : Improving lowcontrast vessels
Adaptive segmentation
Goal : Computation of anoptimal local threshold
Opening
Goal : Removing isolatednoisy points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 1: Vessel Pre-estimation (First approach)
Adaptive Segmentation
CNN-based adaptive segmentation addressed in Rekeczky et al. [1] was proposed.
It consists on a local threshold estimation followed by a locally adaptive segmentation.
Test = αEm + βEv + thres, α ∈ [0, 1], β ∈ [−1, 0]
Em and Ev are the mean and the variance estimations of the considered image
thres is a constant threshold value which depends on the gray-level of the considered image
α and β are scale factors
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 1: Vessel Pre-estimation (First approach)
Adaptive Segmentation
CNN-based adaptive segmentation addressed in Rekeczky et al. [1] was proposed.
It consists on a local threshold estimation followed by a locally adaptive segmentation.
Test = αEm + βEv + thres, α ∈ [0, 1], β ∈ [−1, 0]
thres = max[
∑ Ni=1 Ii1N
,
∑ Ni=1 Ii2N
, . . . ,
∑Ni=1 IiMN
]
Bigger proportion of background pixels than foreground
Mean value of the columns gives a threshold value closer to the local gray level value
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 1: Vessel Pre-estimation (Final version)
Final approach
Histogram equalization has been discarded, since noise is also enhanced
An adaptive segmentation is needed
Diffusion gives a suitable local threshold
The substraction of the original and diffused image gets a suitable segmentation of the image
A local threshold value is used to refine the final result
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 2: Initial contour estimation
1st Approach
Computation of suitable initial conditions for the vessel removing vessel discontinuities
We need to assure that the initial image needed by PLS are completely outside of the vessellocations
Contour-based PLS
Step 1. Dilation : Several dilations are actually needed to remove the discontinuitiesStep 2. Binary edge detection : To get the initial contours
Fitting the exterior of the vessels simplifies the computation of the initial contours
12.7% of pixels belongs to vessels
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 3: External potential estimation (First approach)
1st Approach
Both images contain the needed information in order to stop the PLS evolution in thevessel boundaries
Iext = ρIeq + δIop
ρ and δ are scale factors
Disadvantage : External potential image can be computed in a more accurate way
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 3. External potential estimation (Final approach)
Final Approach
Applying Sobel operator accurate edges are obtained, but:weak vessels are not properly segmentedvessels topology is neither maintained nor assured
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 3. External potential estimation (Final approach)
Final Approach
The image segmented in Stage 1 contains more vessel information and also noise
The combination of both results will gives more robustness to control PLSevolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 3. External potential estimation (Final approach)
Final Approach
A distance estimation is made to guide the evolution towards the vessel locations
Several dilations are performed to compute the distance map
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 3. External potential estimation (Final approach)
Final Approach
A diffusion step is performed to smooth the values
The diffusion step leads towards a loose of accuracy of the boundary location
Edges must be emphasized
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Goals
Input images have been automatically computed from local statistics of the original image
Main parameters of PLS should be calibrated to control the evolution towards the vessels
Good results have been obtained from the image processing point of view
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Final Approach
The input images previously computed are used by PLS to fit the vessel edges
PLS parameters were calibrated
This stage has been split up into several steps to get a better control over the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
1st PLS Step
Balloon Potential has a high relevance compared to the other potentials since vessel locationsare far away
Region merging is enabled
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
Hole Filling operation
This operation is used to remove internal regions appeared due to noise, segmented duringthe previous stages
This operation is applied only once
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Stage 4: PLS evolution
2nd PLS Step
The PLS evolution is guided basically by the external potential due to the proximity to thevessel locations
Region merging is disabled to maintain vessel topology
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Final algorithm: General scheme
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Summary
Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.
Remarks
The final approach is fully implementable on a SIMD processor array
The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task
Notice that the execution time required for PLS cycle has been customized for this particulartask
Only inflation forces are actually needed
Fitting the exterior of the vessels provides a robust control over PLS evolution
All the potentials (forces) have been considered for the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Summary
Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.
Remarks
The final approach is fully implementable on a SIMD processor array
The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task
Notice that the execution time required for PLS cycle has been customized for this particulartask
Only inflation forces are actually needed
Fitting the exterior of the vessels provides a robust control over PLS evolution
All the potentials (forces) have been considered for the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Summary
Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.
Remarks
The final approach is fully implementable on a SIMD processor array
The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task
Notice that the execution time required for PLS cycle has been customized for this particulartask
Only inflation forces are actually needed
Fitting the exterior of the vessels provides a robust control over PLS evolution
All the potentials (forces) have been considered for the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Summary
Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.
Remarks
The final approach is fully implementable on a SIMD processor array
The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task
Notice that the execution time required for PLS cycle has been customized for this particulartask
Only inflation forces are actually needed
Fitting the exterior of the vessels provides a robust control over PLS evolution
All the potentials (forces) have been considered for the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Summary
Original approach Final approachComputing platform CNN SIMDHW implementation Partial CompleteContour representation Contour RegionDesign General purpose Specific purposeExecution time of a PLS cycle 518 µ s. 273 µ s.
Remarks
The final approach is fully implementable on a SIMD processor array
The original version consists on a general purpose version, whereas the final one has beenspecifically tuned for the retinal vessel tree extraction task
Notice that the execution time required for PLS cycle has been customized for this particulartask
Only inflation forces are actually needed
Fitting the exterior of the vessels provides a robust control over PLS evolution
All the potentials (forces) have been considered for the evolution
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Remarks
Due to the high resolution of the retinal images, they have been split up into sub windows
The maximum size allowed in the chip implementations, used in this thesis, is 128x128
The final result is obtained by means of the union of all the sub windows
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
DRIVE database
DRIVE: Digital Retinal Images for Vessel Extraction database
40 images available (7 images with pathologies and 33 images without diseases) with aresolution of 768x584
This database is used for the general analysis of algorithms for the retinal vessel extraction
Only the pixels inside the FOV are actually used to compute the accuracy
The inter-observer agreement is better than in other databases, such as the STARE
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
DRIVE database
DRIVE: Digital Retinal Images for Vessel Extraction database
40 images available (7 images with pathologies and 33 images without diseases) with aresolution of 768x584
This database is used for the general analysis of algorithms for the retinal vessel extraction
Only the pixels inside the FOV are actually used to compute the accuracy
The inter-observer agreement is better than in other databases, such as the STARE
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Experiment design
Software: MATLAB environment
Images from the DRIVE database
Parameters in the stages:
Stage 1
Threshold value established to 5 to refine the results
Stage 2 & 3
A total of 4 erosion steps have been performed
Stage 4
6 cycles were used for the first PLS step
External Pot. Internal Pot. Balloon Pot. No. Cycles1st PLS step 100 % 1% 60% 62nd PLS step 100 % 30% 5% *1
Parameters shown in the table has been tuned only once for all the images *1A convergence control has been implemented, so this number of cycles is not fixed
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Analysis of the accuracy
Maximum Average Accuracy (MAA)
Accuracy =Tpos + Tneg
NP
Tpos is the vessel (true positive) correctlyclassified pixelsTneg is the non-vessel (true negative)correctly classified pixelsNP is the number of pixels considered intothe FOV region
A total number of 20 images (from the test set)has been used
The manual segmentation of the second observerhas been used as the gold standard
Method MAAManual Method 0.9473Soares [2] 0.9466Al-Rawi [3] 0.9458Kirsch [4] 0.9151Staal [5] 0.9611Chaudhuri [6] 0.8773Proposed algorithm 0.9180
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Analysis of the accuracy
Maximum Average Accuracy (MAA)
Accuracy =Tpos + Tneg
NP
Tpos is the vessel (true positive) correctlyclassified pixelsTneg is the non-vessel (true negative)correctly classified pixelsNP is the number of pixels considered intothe FOV region
A total number of 20 images (from the test set)has been used
The manual segmentation of the second observerhas been used as the gold standard
Method MAAManual Method 0.9473Soares [2] 0.9466Al-Rawi [3] 0.9458Kirsch [4] 0.9151Staal [5] 0.9611Chaudhuri [6] 0.8773Proposed algorithm 0.9180
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
SCAMP-3 vision system
Scamp-3 vision system
It provides a high-performance low-powersolution for computer vision applications
The processor array operates in SIMD
The processing elements simultaneouslyexecute identical instructions on their localdata
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
SCAMP-3 vision system
Scamp-3 vision system
The SCAMP-3 vision system executes asequence of simple array instructions
additioninversionone-neighbour access
It operates in a pixel-parallel fashion on128x128 arrays
1.25 MOPS per pixel
Development software and simulatorenvironment
250mW power consumption at themaximum processing
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
SCAMP implementation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Stage 1: Vessel region pre-estimation
Notes
The blurring effect is computed by means of a fast diffusion
The fast diffusion is implemented on the SCAMP via a resistive grid structure
The threshold value is established to zero
The boundary segmentation effect is due to a zero-padded boundaries from thediffusion operation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
SCAMP implementation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Stage 3: External Potential estimation
Notes
This stage has been adapted to the specific performance of the SCAMP
The edge detection over the segmented image is not actually required
The distance estimation is given by the combination of the Sobel and the segmented images
This definition allows us to take advantage of the specific performance of the SCAMP system
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Results using the SCAMP-3 vision system chip
Original image Stage 1 Stage 2 Stage 3 Stage 4
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Results using the SCAMP-3 vision system chip
Original image Stage 1 Stage 2 Stage 3 Stage 4
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Results using the SCAMP-3 vision system chip
Original image Stage 1 Stage 2 Stage 3 Stage 4
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Results using the SCAMP-3 vision system chip
Original image Stage 1 Stage 2 Stage 3 Stage 4
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Results using the SCAMP-3 vision system chip
Original image Stage 1 Stage 2 Stage 3 Stage 4
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Execution time required for a 128x128 sub window
No. Stage Stage Exec. Time ( µs)1 Vessel Region Pre-estimation 12.82 Initial Region Estimation 55.23 External Potential Estimation 134.4
41st PLS Step (6 cycles) 518Hole Filling 1954.52nd PLS Step (40 cycles) 3870.8
Analysis of the execution time
128x128 windows are considered to perform the algorithm in the SCAMP
The I/O time required is 1.25 ms
The execution time for a sub window is 6.5 ms
The global execution time for a retinal image is about 0.1925 s (approximately 30 subwindows)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Execution time required for a 128x128 sub window
No. Stage Stage Exec. Time ( µs)1 Vessel Region Pre-estimation 12.82 Initial Region Estimation 55.23 External Potential Estimation 134.4
41st PLS Step (6 cycles) 518Hole Filling 1954.52nd PLS Step (40 cycles) 3870.8
Analysis of the execution time
128x128 windows are considered to perform the algorithm in the SCAMP
The I/O time required is 1.25 ms
The execution time for a sub window is 6.5 ms
The global execution time for a retinal image is about 0.1925 s (approximately 30 subwindows)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Summary
Method MAA Exec. TimeManual Method 0.9473 2 h.Soares [2] 0.9466 3 min.Al-Rawi [3] 0.9458 5 s.Kirsch [4] 0.9151 2 s.Staal [5] 0.9611 15 min.Chaudhuri [6] 0.8773 5 s.Proposed algorithm 0.9180 0.1925 s.
Analysis of the execution time
The MAA is suitable for many practical applications
A very fast system compared with standard approaches
The proposed algorithm has been recently implemented in C, using Microsoft Visual Studio9.0
The execution time required is 6.64 s in a PC with a Intel 2 Core Duo processor at 2.10GHzNo sub windowing is needed in this case
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Summary
Method MAA Exec. TimeManual Method 0.9473 2 h.Soares [2] 0.9466 3 min.Al-Rawi [3] 0.9458 5 s.Kirsch [4] 0.9151 2 s.Staal [5] 0.9611 15 min.Chaudhuri [6] 0.8773 5 s.Proposed algorithm 0.9180 0.1925 s.
Analysis of the execution time
The MAA is suitable for many practical applications
A very fast system compared with standard approaches
The proposed algorithm has been recently implemented in C, using Microsoft Visual Studio9.0
The execution time required is 6.64 s in a PC with a Intel 2 Core Duo processor at 2.10GHzNo sub windowing is needed in this case
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Overlapping
Goal
This technique has been analyzed to study the improvement of the global MAA value
Rows and columns of the sub windows are overlapped to get redundant information in thelimit areas
The importance of the pixel information depends on the position of that pixel in the sub window
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Column overlapping
Column Overlapping
Columns are overlapped, and the value of the pixels are weighted according to its position insidethe overlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Column overlapping
Column Overlapping
Columns are overlapped, and the value of the pixels are weighted according to its position insidethe overlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Column overlapping
Column Overlapping
Columns are overlapped, and the value of the pixels are weighted according to its position insidethe overlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Row overlapping
Row Overlapping
Rows are overlapped, and the value of the pixels are weighted according to its position inside theoverlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Row overlapping
Row Overlapping
Rows are overlapped, and the value of the pixels are weighted according to its position inside theoverlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Row overlapping
Row Overlapping
Rows are overlapped, and the value of the pixels are weighted according to its position inside theoverlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Row overlapping
Row Overlapping
Rows are overlapped, and the value of the pixels are weighted according to its position inside theoverlapping area
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
Application of the overlapping to the results of the algorithm
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Reliability AnalysisTime performance Analysis: SCAMP implementationOverlapping technique
0
0.2
0.4
0.6
0.8
1
64 32 16 8 4 0
No. Pixels used in the Overlapping
LegendSensitivitySpecificityAccuracy
0
0.2
0.4
0.6
0.8
1
64 32 16 8 4
Exe
c. T
ime
(s.)
No. Pixels used in the Overlapping
LegendExecution Time
Conclusions
The overlapping technique is not actually needed due to the remarkable increment onthe execution time and the slightly improvement in the other factors.
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Retinal vessel tree is used in a wide range of practical applicationsMedical researchMedical systems use the retinal vessel features for example in early diagnosis, related with:
stenosismalformationscardiovascular risk
Biometric authentication
Vessel-pattern is being used for authentication systems due to its robustness againstforgery
The pixel-parallel tree extraction algorithm
The algorithm proposed in this thesis has been included into the following applications:
Authentication applications:
Creases-based authentication systemPoint feature-based authentication system
Medical applications:
Arteriolar-to-venular diameter ratio estimation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Retinal vessel tree is used in a wide range of practical applicationsMedical researchMedical systems use the retinal vessel features for example in early diagnosis, related with:
stenosismalformationscardiovascular risk
Biometric authentication
Vessel-pattern is being used for authentication systems due to its robustness againstforgery
The pixel-parallel tree extraction algorithm
The algorithm proposed in this thesis has been included into the following applications:
Authentication applications:
Creases-based authentication systemPoint feature-based authentication system
Medical applications:
Arteriolar-to-venular diameter ratio estimation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Traditional modes of authentication
Physical possessions : keys, passports, smart cards
Knowledge : password, pass phrases
Biometric features : physiological and behavioral characteristics of individuals thatdistinguish one person from the next
Characteristics for a biometric feature
Universality
Uniqueness
Permanence: invariant over the time
Collectability: it should be measurable
Acceptability by the users / individuals
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
The pixel parallel retinal vessel extraction algorithm in theauthentication systems
Notes
Two authentication systems have been considered to integrate the pixel parallel approach 1
Goal: Improving the computation time for obtaining the retinal vessel tree
Both of these systems use the skeleton instead of the retinal vessel tree
An skeletonisation step has been included to process the output of the pixel-parallel algorithm
1The authentication systems is the basis of the thesis of Marcos Ortega Hortas
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Authentication system using creases
Image registration
Steps for the computation of the similarity value:
Alignment of the image under study and the reference image
Normalized cross-correlation function:
γ =
∑
x,y [f (x, y) − f ][g(x, y) − g]√
[f (x, y) − f ]2[g(x, y) − g]2
g is the mean of the registered imagef is the mean of the image under studyOnly the pixels belonging to the overlapping area are not null
A threshold is defined to distinguish the individuals
If γ is higher than the threshold that means that both images belong to the sameindividual, and vice versa
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Authentication system using point feature extraction
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Authentication system using point feature extraction
Feature point extraction
Feature points are:
Ridge endingsBifurcations of the vessels
Some steps have been defined
1 Segment detectionDetecting the segmentsLabeling the segments
2 Detection of union and bifurcations3 Feature point sets is computed
Unions
Endpoints are close to each other and they havesimilar orientations with a smooth connection
Bifurcations
Compute the endpoint directionExtend the segment in that directionIf a segment is found in that direction, the bifurcationis tagged
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Authentication system using point feature extraction
Registration process
Transformation of the acquired image in order to align its feature points with thereference imageTransformation considered is the Similarity Transformation (ST) which can handle:
TranslationIsotropic scalingRotation
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Authentication system using point feature extraction
Matching
Similarity between points (A and B) stands for the maximum distance allowed
S(A, B) = 1 −distance(A, B)
D
If two points have a similar value to the reference point, the best match iscomputed by meas of the probability of correspondence
The matching value is computed as follows
1√
MN
∑
(i,j)∈Q
S(Ai , Bj )
The matching value is compared with the threshold value for the acceptance orrejection
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Experiment design
Blind test has been designed
100 images (12 of them belonging to different individuals)
Image resolution is 768x584 pixels, which implies a total number of 30 subwindows
The execution time needed for the extract the retinal vessel tree is 0.1925 s. (6.5ms for each sub window)
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Experimental results
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Experimental results
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Similarity Threshold for Acceptance
Err
or R
ate
FAR
FRR
ConfidenceBand
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Threshold
Per
cent
age
FARFRRERR
Conclusions
False Acceptance (FAR) and False Rejection (FRR) rates can be reduced to 0 (FAR = FRR)
Equal Error Rate (EER) = 0 which implies a 100% of effectiveness
The mean execution time is about 0.19 s to get the skeleton, 250 ms. for the authenticationstage
The whole execution time for the authentication system is 0.44 s.
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Experimental results
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Similarity Threshold for Acceptance
Err
or R
ate
FAR
FRR
ConfidenceBand
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Threshold
Per
cent
age
FARFRRERR
Conclusions
False Acceptance (FAR) and False Rejection (FRR) rates can be reduced to 0 (FAR = FRR)
Equal Error Rate (EER) = 0 which implies a 100% of effectiveness
The mean execution time is about 0.19 s to get the skeleton, 250 ms. for the authenticationstage
The whole execution time for the authentication system is 0.44 s.
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Medical applications
Vessel geometry are the basis of medical applications related with:early diagnosiseffective monitoring of therapies in retinopathy
The Arteriolar-to-Venular ratio (AVR) is used to establish the cardiovascular risk,for example
The pixel-parallel algorithm
The pixel parallel algorithm proposed in this thesis has been integrated into theSIRIUS web application (System for the Integration of Retinal ImagesUnderstanding Services)
5 hospitals in Galicia are currently using this web application
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Medical applications
Vessel geometry are the basis of medical applications related with:early diagnosiseffective monitoring of therapies in retinopathy
The Arteriolar-to-Venular ratio (AVR) is used to establish the cardiovascular risk,for example
The pixel-parallel algorithm
The pixel parallel algorithm proposed in this thesis has been integrated into theSIRIUS web application (System for the Integration of Retinal ImagesUnderstanding Services)
5 hospitals in Galicia are currently using this web application
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
SIRIUS and the pixel parallel algorithm
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Remark
The segment which joins the points must be perpendicular to the centreline of thevessel
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points areobtained
6 Estimation of the vesseldiameter
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points areobtained
6 Estimation of the vesseldiameter
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Vessel width
W =
3∑
i=1
li /3
Euclidean distance
l1 =√
(xA − xB)2 + (yA − yB)2
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points areobtained
6 Estimation of the vesseldiameter
7 Manual classification of the twotypes of vessels into vein orartery
8 Computation of the AVR ratio
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points areobtained
6 Estimation of the vesseldiameter
7 Manual classification of the twotypes of vessels into vein orartery
8 Computation of the AVR ratio
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Steps in the system
1 Selection of the retinal imageby the specialist
2 Concurrently extraction of theretinal vessel tree
3 Selection of the optic disk
4 Drawing three circles,concentric to the optic disk
5 Obtaining crossing points areobtained
6 Estimation of the vesseldiameter
7 Manual classification of the twotypes of vessels into vein orartery
8 Computation of the AVR ratio
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Vessel width
W =
3∑
i=1
li /3
Euclidean distance
l1 =√
(xA − xB)2 + (yA − yB)2
AVR ratio
AVR =
∑
Wa∑
Wv
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Authentication applicationsAVR ratio estimation application
Experimental results
No. Image Caderno et al. [7] Proposed algorithm1 0.79 0.782 0.82 0.843 0.81 0.824 0.80 0.765 0.82 0.806 0.89 0.917 0.77 0.778 0.90 0.939 0.83 0.8010 0.83 0.81
Experimental conditions
Images obtained using the Cannon CR6-45NM Non-Mydriatic Retinal Camera
Ten images with a resolution of 768x584 pixels
The pixel parallel retinal vessel tree extraction algorithm takes 0.19 s for the wholeangiography
The proposal of Caderno et al. [7] takes 32.1 s for extracting the whole retinal vessel tree
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Outline
1 IntroductionMedical images: an overviewVessel extraction techniques: State of the artA pixel-parallel approach
2 Pixel Level Snakes (PLS)Contour-based approachRegion-based approach
3 Pixel parallel retinal vessel tree extraction algorithm
4 Experimental resultsReliability Analysis: DRIVE databaseTime performance Analysis: SCAMP implementationOverlapping technique
5 ApplicationsAuthentication applicationsAVR ratio estimation application
6 Conclusions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
A novel algorithm for retinal vessel tree extraction has been presentedIt has been implemented in terms of local operations and convolutionsImage processing point of view
DRIVE database has been used to test the reliability of the proposed algorithmMAA obtained shows that the results of the proposed algorithm is enough for manypractical applications
Time performance point of viewThe algorithm has been implemented in the SCAMP systemThe execution time study shows that it is faster than conventional PC-based techniques
This algorithm has been successfully integrated into practical applicationsAuthentication applications A 100% of effectiveness is maintained in theauthentication processAVR ratio estimation Faster computation of the AVR ratio with similar results
Future research
Integration of this algorithm into
Applications with fast computation requirementsvideo-based applications (tracking)
Projection onto a focal plane processing systems
Full integration with other devices for making a portable device
The evolution on processor arrays in the increment of their size will balance the distancebetween PC-based and Hardware-based solutions
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Rekeczky, C., Schultz, A., Szatmari, I., Roska, T., Chua, L.O.:Image Segmentation and Edge Detection via Constrained Diffusion and AdaptiveMorphology: a CNN approach to Bubble/debris Image Enhancement.In: Proc. 6th Int. Symp. Nonlinear Theory and its Applications (NOLTA ’97). (1997)209–212
Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J.:Retinal Vessel Segmentation using the 2-D Gabor Wavelet and SupervisedClassification.IEEE Trans. Med. Imag. 25 (2006) 1214–1222
Al-Rawi, M., Qutaishat, M., Arrar, M.:An improved matched filter for blood vessel detection of digital retinal images.Comput. Biol. Med. 37(2) (2007) 262–267
Kirsch, R.A.:Computer Determination of the Constituent Structure of Biological Images.Comp. Biomed. Res. 4(3) (1971) 315–328
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.:Ridge-Based Vessel Segmentation in Color images of the Retina.IEEE Trans. Med. Imag. 23 (2004) 501–509
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
IntroductionPixel Level Snakes (PLS)
AlgorithmExperimental results
ApplicationsConclusions
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.:Detection of Blood Vessels in Retinal Images using Two-Dimensional MatchedFilters.IEEE Trans. Med. Imag. 8 (1989) 263–269
Caderno, I.G., Penedo, M.G., Marino, C., Carreira, M.J., Gomez-Ulla, F.,Gonzalez, F.:Automatic Extraction of the Retina AV Index.In: LNCS Int. Conf. Image Analysis and Recog. (ICIAR’04). Volume 3212. (2004)132–140
Carmen Alonso Montes Automatic Pixel-Parallel Retinal Vascular Tree Extractio n
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