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Presented by,Md.Mintu pk.Roll : 1209050 Dept of ECE (KUET).
Dept. of ECE ( KUET )Date : 09.08.2016 Page: 01
Automatic Detection of Blood Vessel in Retinal Images
Author,A.ELBALAOUI, M. FAKIR Faculty of Science and Technology, Sultan Moulay Slimane University Beni Mellal, Morocco.
Source: IEEE transaction on computer grapics,imaging ,and visualization. March 29 2016-April 1 2016
CONTENTS…
Page: 02
Project vision
Methodology Datasheets
Preprocesing
Vessel detection
Hessian multiscale enhancement filter
Adaptive thresholding
Result analysis
Conclusion
Dept. of ECE ( KUET )Date : 09.08.2016
PROJECT VISION..
Page: 03
Detection of Blood vessel.
Measurement of vessel diameter.But why ??
For diagnosis and treatment retinal disease such as,
Diabetic Retinopathy Glaucoma Hypertensive Retinopathy
Dept. of ECE ( KUET )Date : 09.08.2016
CONTINUE....
Page: 04
Fig 01 : Anatomy of eye Fig 02: Fundus image of retina
Dept. of ECE ( KUET )Date : 09.08.2016
CONTINUE....
Page: 05
Blood Vessel Damage Precedes Vision Loss.Normal Retina Diabetic Retinopathy Hypertensive Retinopathy
Vision Loss
Dept. of ECE ( KUET )Date : 09.08.2016
BLOOD VESSEL DETECTION METHOD..
Page: 06
There are three parts of proposed method..
Preprocessing Retinal Image
Vesselness Filter
Hessian Multiscale Enhancement Filter
1
2
3
Dept. of ECE ( KUET )Date : 09.08.2016
BLOOD VESSEL DETECTION STEPS..
Page: 07
Fig 03: Block diagram of blood vessel detection method
Dept. of ECE ( KUET )Date : 09.08.2016
Page: 08
DATASETS..
Performance of algorithm compared three database..
DRIVE Database ( Digital Retinal Images for Vessel Extraction ) Consists of 40 color images. Images size 565×584 pixels. Spatial resolution of 20 micro/pixel.
STARE database(STructured Analysis of the Retina) Contains 81 fundus images. 31 images of healthy retinas. 50 images of retinas with disease.
CHASE_DB1 database Contains 28 color images. 14 patients in the program Child Heart. Resolution of 1280x960 pixels
Dept. of ECE ( KUET )Date : 09.08.2016
PREPROCESSING METHOD..
Page: 9
Fig 04: Original RGB retinal image Fig 05: Red, green, and blue histogram
Histogram
Dept. of ECE ( KUET )Date : 09.08.2016
PREPROCESSING STEPS..
Page: 10
Fig 06: (a) Original RGB retinal image . (b) Extract green Channel. (c) Image after CLAHE and dilate borders. (d) Enhanced image (e) zooming in rectangular region before dilate borders. (f) Zooming in rectangular region after dilated borders
DRIVE
STARE
Dept. of ECE ( KUET )Date : 09.08.2016
VESSEL DETECTION STEPS....
Enhancement filtering equation :
Hessian enhancement filtering
Where
Page: 11Dept. of ECE ( KUET )Date : 09.08.2016
COMPARISON DIFFERENT STEPS FOR VESSEL ENHANCEMENT..
Page: 12
Pixels
Inte
nsity
Pixels
Inte
nsity
Pixels In
tens
ity
a b
c d
Fig 07 : (a) original image, and (b) the intensity distribution of a vessel. (c)–(d) compare the enhanced performance.
Dept. of ECE ( KUET )Date : 09.08.2016
RESULT ANALYSIS..
Page: 13
Detection results: From DRIVE database
Fig 08 :(a) Retinal images from DRIVE database, (b) results of the proposed method, (c) the ground truth manual.
( a ) ( b ) ( c )
Dept. of ECE ( KUET )Date : 09.08.2016
CONTINUE…
Page: 14
Detection results: From STARE database
Fig 09 : (a) Retinal images from STARE database, (b) results of the proposed method (c) the ground truth manual.
( a ) ( b ) ( c )
Dept. of ECE ( KUET )Date : 09.08.2016
CONTINUE…
Page: 15
Detection results: From CHASE_DB1 database
Fig 10 : (a) Retinal images from CHASE_DB1 database, (b) the ground truth manual, (c) results of the proposed method.
( a )
( a ) ( b ) ( c )
Dept. of ECE ( KUET )Date : 09.08.2016
PERFORMANCE PARAMETERS FOR PROPOSED METHOD.
Page: 16
Parameter Equation Supervised method
Proposed method
Sensitivity 0.7332 0.7630
Specificity 0.9782 0.9713
Accuracy 0.9466 0.9443
Precision 0.6923 0.6835
F-measure 0.6372 0.6293
Dept. of ECE ( KUET )Date : 09.08.2016
PERFORMANCE PARAMETERS..
Page: 17
Fig 11 : performance evaluation for DRIVE dataset.
Dept. of ECE ( KUET )Date : 09.08.2016
PERFORMANCE PARAMETERS..
Page: 18
Fig 12 : performance evaluation for STARE dataset.
Dept. of ECE ( KUET )Date : 09.08.2016
CONCLUSION..
Page: 19
Detected retinal blood vessels .
Tested on the DRIVE, STARE and CHASE_DB1
databases.
Result higher than many of the state-of-the-art methods
Method applicable to all types of retinal images,
healthy as well as abnormal
So,Proposed Method succesfully..
Dept. of ECE ( KUET )Date : 09.08.2016
REFERENCE..
[1] A.M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1200-1213, 2006. [2] Ana Salazar-Gonzalez, Djibril Kaba, Yongmin Li, and Xiaohui Liu, “Segmentation of the Blood Vessels and Optic Disk in Retinal Images,” IEEE J. Biomedical and Health Informatics vol. 18, no. 6, pp. 1874-1886, 2014. [3] Leung H, Wang JJ, Rochtchina E, Wong TY, Klein R, Mitchell P (2003) Impact of current and past blood pressure on retinal arteriolar diameter in olderpopulation. J Hypertens: 1543 1549. [4] Wang JJ, Taylor B, Wong TY, Chua B, Rochtchina E, Klein R, Mitchell P (2006) Retinal vessel diameters and obesity: a population- based study in older persons. Obese Res: 206–214.
Page: 20Dept. of ECE ( KUET )Date : 09.08.2016
Page: 21
Thanks for Your Attention.
Dept. of ECE ( KUET )Date : 09.08.2016