539 Image Processing BV Extraction

Automatic Detection of Blood Vessels Automatic Detection of Blood Vessels in Digital Retinal Image using in Digital Retinal Image using CVIP Tools CVIP Tools

Krishna Praveena Mandava Krishna Praveena Mandava

Sri Swetha KantamaneniSri Swetha Kantamaneni

Robert LeAnderRobert LeAnder

OverviewOverview

The DevastationThe Devastation Diabetic retinopathy – 4.1 million US Adults Diabetic retinopathy – 4.1 million US Adults

National Health Interview Survey and US Census National Health Interview Survey and US Census PopulationPopulation

Glaucoma – 2 million individuals in the US. Glaucoma – 2 million individuals in the US.

Ophthalmologic imagesOphthalmologic images Important structures – Important structures – Blood VesselsBlood Vessels Help detect and treat Eye Diseases affecting Help detect and treat Eye Diseases affecting

blood vesselsblood vessels

OverviewOverview

Damaged blood vessels indicate retinal disease.Damaged blood vessels indicate retinal disease. Blood clots indicate diabetic retinopathy. Blood clots indicate diabetic retinopathy. Narrow blood vessels indicate Central Retinal Artery Narrow blood vessels indicate Central Retinal Artery

Occlusion.Occlusion.

Observation of blood vessels in retinal imagesObservation of blood vessels in retinal images Shows presence of diseaseShows presence of disease Helps prevent vision loss by early detectionHelps prevent vision loss by early detection

The Need for the StudyThe Need for the Study

Automated Blood Vessel Extraction algorithms Automated Blood Vessel Extraction algorithms can save time, patients’ vision and medical can save time, patients’ vision and medical

costs.costs.

Effects of Diseases on Blood VesselsEffects of Diseases on Blood Vessels

Image of Diseased Retina Due to DiabetesImage of Diseased Retina Due to Diabetes

Disease produces hemorrhages, exudates and micro aneurysms (dark red spots).

Central Retinal Artery Occlusion (CRAO)Central Retinal Artery Occlusion (CRAO)

Results in Results in narrowing blood narrowing blood vessels.vessels.


Branch Retinal Artery Occlusion (BRAO)Branch Retinal Artery Occlusion (BRAO)

Where artery branch points are occluded or blocked


6 Approaches to Blood Vessel Extraction6 Approaches to Blood Vessel Extraction

1.1. Pattern recognition techniquesPattern recognition techniques

2.2. Model based approachesModel based approaches

3.3. Tracking based approachesTracking based approaches

4.4. Artificial intelligence based approachesArtificial intelligence based approaches

5.5. Neural network based approachesNeural network based approaches

6.6. Miscellaneous tube-like object detection Miscellaneous tube-like object detection approaches.approaches.

1. Pattern recognition techniquesDeals with automatic detection or classification of objects or features.

A.Multi scale approaches

Based on image resolution. Major vessels are extracted from low resolution images and minor vessels from high resolution images.

B.Skeleton based approaches

Vessel centerlines are extracted and then connected to create a vessel tree.

C. Ridge-Based Approaches

This is specialized skeleton based approaches. Ridges are peaks.


D. Region growing approaches…

• Assume that pixels are close to each other and have similar intensity values and are likely to belong to same objects.

• Start region growth from a seed point, then segment the image based on some predefined criterion.

• Have the Disadvantage that the seed point should be selected manually.

E. Differential-Geometry-based approaches…

• Utilizes techniques developed from the complex

mathematical field of Differential Geometry

• Are based on blood-vessel structural properties

1. 1. Pattern recognition techniques

F. Matched-Filter Approaches

• Are signal processing approaches where new images with un-extracted vessels are convolved with known profiles of vessels.

• Matched filters are followed by image processing operations like

thresholding to get the final vessel contours.

G. Morphology Schemes…

• Apply structuring elements to images to effect dilation and erosion are two main operations.

• Include Top Hat and Watershed algorithms.


2. Model-Based Approaches…

• Include Snakes algorithms, which are the primary types of algorithms used for vessel extraction.

• A “Snake” is an active (deformable) contour with a set of Control Points

connecting the segments of the contour to each other.

• It is a user interactive algorithm.

3. Tracking-Based Approaches… Are similar to pattern recognition approaches except they apply local, instead

of global operator

analyzing the pixels orthogonal to the tracking direction.

4.4. Artificial intelligence-based approaches…Artificial intelligence-based approaches… Use prior knowledge of model vessel structures to determine vessel structures Use prior knowledge of model vessel structures to determine vessel structures

in the “unextracted” (unsegmented) image.in the “unextracted” (unsegmented) image.

Some applications may use a general blood vessel model for extraction .Some applications may use a general blood vessel model for extraction .

5.5. Neural Network-Based approaches…Neural Network-Based approaches… Use neural networks as a classification method. The system is trained using a Use neural networks as a classification method. The system is trained using a

set of images having blood vessel contours. The target image is set of images having blood vessel contours. The target image is segmented using the trained systemsegmented using the trained system

6.6. Miscellaneous Tube-Like Object Detection Miscellaneous Tube-Like Object Detection Approaches…Approaches…

• Deals with the extraction of tubular structures from images.Deals with the extraction of tubular structures from images.

• Are not designed for vessel extraction.Are not designed for vessel extraction.

RETINAL BLOOD VESSEL EXTRACTION (SEGMENTATION)

Available Image Databases

DRIVE and STARE databases are available for the public. http://www.ces.clemson.edu/~ahoover/stare/ http://www.parl.clemson.edu/stare/nerve/

We worked on 50 fundus images from the STARE database.

How the Images Were TakenAn Optical camera is used to see through the pupil of the eye to the inner surface

of the eyeball. The resulting retinal image shows the optic nerve, fovea, and the blood vessels.

Available Image Databases

DRIVE and STARE databases are available for the public. http://www.ces.clemson.edu/~ahoover/stare/ http://www.parl.clemson.edu/stare/nerve/

We worked on 50 fundus images from the STARE database.

How the Images Were TakenAn Optical camera is used to see through the pupil of the eye to the inner surface

of the eyeball. The resulting retinal image shows the optic nerve, fovea, and the blood vessels.

MethodsMethods

Steps used blood vessel extraction…Steps used blood vessel extraction… PreprocessingPreprocessing Extraction (segmentation)Extraction (segmentation) Post processingPost processing

Software:

We used Computer Vision and Image Processing Tools to apply various algorithms to extract (segment) blood vessels.

Our Project

Preprocessing:Preprocessing:

Preprocessing will eliminate errors caused Preprocessing will eliminate errors caused during taking the image and to reduce during taking the image and to reduce brightness effects on the image .brightness effects on the image .

The original images are resized from The original images are resized from 150*130 to 256*256 to use in CVIP tools. 150*130 to 256*256 to use in CVIP tools.

Images in green bands show vessel Images in green bands show vessel structures most reliably. So, the green structures most reliably. So, the green band was extracted.band was extracted.

Extraction of blood vessels:Extraction of blood vessels:

Tools that we applied: Tools that we applied: Median filters Median filters Laplacian filters Laplacian filters Image enhancement methods like Adaptive Contrast Image enhancement methods like Adaptive Contrast

Enhancement, Histogram equalization.Enhancement, Histogram equalization. Edge detection like Canny edge detection. Edge detection like Canny edge detection.

Post processing:Post processing:

The output images from blood vessel The output images from blood vessel extraction were processed to get clearer extraction were processed to get clearer contours of the vessels.contours of the vessels.

The following techniques were applied The following techniques were applied Sharpening by high pass spatial filters Sharpening by high pass spatial filters Smoothing by FFT smoothing, Ypmean filterSmoothing by FFT smoothing, Ypmean filter

Original Image and Expected Output:Original Image and Expected Output:

Our final images for different algorithms:

Exp 1 Exp 2 Exp 3

Exp 4 Exp 5

Summary:Summary:

NEED AND USE: Extraction of blood vessels NEED AND USE: Extraction of blood vessels

Research is ongoing and there is still a great Research is ongoing and there is still a great need to develop for an easier, more accurate need to develop for an easier, more accurate and useful algorithms.and useful algorithms.

We were able to detect major blood vesselsWe were able to detect major blood vessels

Better algorithms can be developed using CVIP Better algorithms can be developed using CVIP tools for the extraction of minor blood vessels.tools for the extraction of minor blood vessels.

Suggestions for Future WorkSuggestions for Future WorkDevelop techniques for not only better detection of Develop techniques for not only better detection of vessel edges, but for filling in the vessels so that they vessel edges, but for filling in the vessels so that they are more anatomically exacting regarding medical image are more anatomically exacting regarding medical image standards. standards. As only edges are detected they can be filled As only edges are detected they can be filled to get the blood vessel. Research should be done in to get the blood vessel. Research should be done in filling the structures in our final outputs.filling the structures in our final outputs.Develop better algorithms based advantages that may Develop better algorithms based advantages that may be given by the following vessel structural properties (as be given by the following vessel structural properties (as mentioned in a few papers): mentioned in a few papers):

Vessel size may decrease when moving away from the Vessel size may decrease when moving away from the optic disc and the width of blood vessels may lie with in optic disc and the width of blood vessels may lie with in 2-10 pixels2-10 pixels

Vessels are darker relative to the background.Vessels are darker relative to the background. The intensity profile varies from vessel to vessel by a The intensity profile varies from vessel to vessel by a

small value. That profile is modeled as a Gaussian small value. That profile is modeled as a Gaussian shape. shape.

More Suggestions for Future WorkMore Suggestions for Future Work

Extraction of Minute blood vessels.Extraction of Minute blood vessels.Extracted outputs can be verified by an ophthalmologistExtracted outputs can be verified by an ophthalmologistExtraction outputs may also be calculated of sensitivity Extraction outputs may also be calculated of sensitivity and specificity of blood vessels will give you better final and specificity of blood vessels will give you better final results.results.Detection of the optic disc is also needed as the border Detection of the optic disc is also needed as the border of the disc appears as a blood vessel. To prevent this of the disc appears as a blood vessel. To prevent this the optic disc should be detected and removed before the optic disc should be detected and removed before blood vessels are extracted.blood vessels are extracted.Blood vessels should be separated from hemorrhages, Blood vessels should be separated from hemorrhages, and micro aneurysms. and micro aneurysms.

Conclusion:Conclusion:

CVIPtools is a very handy method for CVIPtools is a very handy method for applying extraction techniques. There is a applying extraction techniques. There is a dire need for easier methods of blood dire need for easier methods of blood vessel extraction. CVIPtools may provide vessel extraction. CVIPtools may provide accurate automatic detection algorithms accurate automatic detection algorithms for clinical applications in retinopathy. for clinical applications in retinopathy.

Reference:Reference:

1. 1. Computer Imaging Digital Image Analysis and ProcessingComputer Imaging Digital Image Analysis and Processing

- Dr. Scott E Umbaugh- Dr. Scott E Umbaugh

2. 2. Digital Image Processing Digital Image Processing - Rafael C .Gonzalez, Richard - Rafael C .Gonzalez, Richard

E .WoodsE .Woods

3. A Review of Vessel Extraction Techniques and Algorithms 3. A Review of Vessel Extraction Techniques and Algorithms

– – Cemil Kirbas and Francis Quek, Wright State University, Dayton, Cemil Kirbas and Francis Quek, Wright State University, Dayton, OhioOhio

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– – Kenneth W.Tobin, Edward Chaum, Priya Govindaswami, Thomas Kenneth W.Tobin, Edward Chaum, Priya Govindaswami, Thomas P.Karnowski, Omer Sezer, University of Tennessee, Knoxville, Tennessee.P.Karnowski, Omer Sezer, University of Tennessee, Knoxville, Tennessee.

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S. Lal and D. Usher.S. Lal and D. Usher.14.Segmentation of retinal blood vessels by combining the detection of 14.Segmentation of retinal blood vessels by combining the detection of

centerlines and morphological reconstruction centerlines and morphological reconstruction – –Ana Maria Mendonca, Aurelio Campilho members IEEE.Ana Maria Mendonca, Aurelio Campilho members IEEE.15. 15. The Eye Diseases Prevalence Research Group. The prevalence of diabetic

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539 Image Processing BV Extraction