An Image Processing Based Approach to Automatic Detection of Retina Layers Using Texture Analysis

In this paper, a computer approach is proposed for recognition of retina layers on optical coherence tomography(OCT) images. OCT uses the optical backscattering of light to scan the eye and describe a pixel representation of the anatomic layers within the retina. Our approach is based on co-occurrence matrix for feature extraction and a supervised learning method for classification, which four features of this matrix have been used as a feature vector by support vector machine (SVM) has been used for segmentation retina layers. Achieved result of combined these two methods in the best state was 98.6% precision. This result shows that apply these methods on OCT images discriminate retina layers with efficient accuracy. Since, recognition of retina layers is important for automatic analyzing of OCT images, therefore our proposed methods can be very useful.

Keywords:

Texture analysis , co-occurrence matrix , Support vector machine , Image segmentaion , optical coherence omography