  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.
