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Optic disk segmentation using texture
S Mohammed, T Morris, N Thacker
In: 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; 05 Jan 2014-08 Jan 2014; Lisbon. 2014.
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Abstract
The paper describes our work on the segmentation of the optic disc in retinal images. Our approach comprises of two main steps; a pixel classification method to identify pixels that may belong to the optic disc boundary and a circular template matching to estimate the circular approximation of the optic disc boundary. The pixel’s features used is based on texture, calculated using the intensity differences of local image patches. This was adapted from Binary Robust Independent Elementary Features (BRIEF). BRIEF is inherently invariant to image illumination and has a lower degree of computational complexity compared to other existing texture measurement methods. Fuzzy C-Means (FCM) and Naive Bayes are the clustering and classifier used to cluster/classify the image pixels. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images. The average mean overlap ratio between the true optic disc region and segmented region is 0.81 for both FCM and Naive Bayes. Comparison with a method based on the Hough Transform is also provided.