Comparison Different Vessel Segmentation Methods in Automated Microaneurysms Detection in Retinal Images using Convolutional Neural Networks

Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using a combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using a combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found a sensitivity of more than 85% in the detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found a sensitivity of more than 80% in the detection of MAs for all 140 images of two databases. And lastly, using the Matched filter, our algorithm found a sensitivity of more than 87% in the detection of MAs in both local and DRIVE datasets.Comment: arXiv admin note: substantial text overlap with arXiv:2004.09493, arXiv:2005.09098, arXiv:2004.10253, arXiv:2004.1169

The Efficacy of Microaneurysms Detection With and Without Vessel Segmentation in Color Retinal Images

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 caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for the identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for the 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 detection and segmentation are as follows. In 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 the MAs detection step using a 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 a sensitivity of more than 85% in the detection of MAs with 11 false-positive rates 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 a sensitivity of about 90% in the 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.Comment: arXiv admin note: substantial text overlap with arXiv:2004.09493, arXiv:2005.0909