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Proceedings Paper

The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images
Author(s): Meysam Tavakoli; Mahdieh Nazar; Alireza Mehdizadeh
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Paper Abstract

Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143Y (16 March 2020); doi: 10.1117/12.2548527
Show Author Affiliations
Meysam Tavakoli, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Mahdieh Nazar, Shahid Beheshti Univ. of Medical Sciences (Iran, Islamic Republic of)
Alireza Mehdizadeh, Shiraz Univ. of Medical Sciences (Iran, Islamic Republic of)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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