Automatic Detection of Microaneurysms in OCT Images Using Bag of Features

• URL: http://arxiv.org/abs/2205.04695v1
• Date: Tue, 10 May 2022 06:43:01 GMT
• Title: Automatic Detection of Microaneurysms in OCT Images Using Bag of Features
• Authors: Elahe Sadat Kazemi Nasab, Ramin Almasi, Bijan Shoushtarian, Ehsan Golkar, Hossein Rabbani
• Abstract summary: Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. This work is done using the dataset collected from FA and OCT images of 20 patients with DR.
• Score: 8.777674946755717
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical Coherence Tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina and it has been used in recent years to diagnose many eye diseases. As a result, in this paper has attempted to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly Fluorescein Angiography (FA) and OCT images were registered. Then the MA and normal areas were separated and the features of each of these areas were extracted using the Bag of Features (BOF) approach with Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAsautomatically is a new idea and the results obtained as preliminary research in this field are promising .

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