Evaluation of Retinal Optic Disc Segmentation in Patients with Glaucoma and Comparison with Other Methods of Medical Image Processing

Introduction: Glaucoma is the most common cause of blindness in some countries. In the meantime, the field of retinal image processing has been proposed in order to provide automatic systems for disease diagnosis. Among the methods of medical image processing, image segmentation is a process of identification and change in the display of an image. The objective of this study was to use the segmentation method and compare it with previous algorithms so as to be able to diagnose retinal optic disc more accurately. Method: In the present analytical study, using the image segmentation method, each pixel was assigned a label in such a way that pixels with the same label had similar characteristics. The optic disc segmentation was performed. Using MATLAB software, retinal images of patients with glaucoma were entered into the program and an ideal output was obtained. Results: The quantitative analysis of the obtained results showed a high accuracy (85%) for the proposed method for the segmentation of the retinal optic disc; thus, the results can be used to efficiently diagnose a person with glaucoma. Conclusion: The purpose of segmenting an image is to make raw data more usable for subsequent statistical processing. It is expected that in the future, feature extraction be more accurate, and more details be available to machine vision systems to identify objects in the images.