A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis

Medical image classification system is widely used by the radiologists to segment the medical images into meaningful regions. Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Automatic detection of diabetic retinopathy in retinal image is vital as it delivers data about unusual tissues which is essential for planning treatment. Automating, this method is challenging due to the high variabiity in the appearance of tissue among dissimilar patients and in many circumstances, the comparison between abnormal and normal tissue. This paper presents a new methodology and a computerized diagnostic system for diabetic retinopathy. In this article, adaptive histogram equalization is used to convert colour images to gray scale images followed by significant features are selected using hybrid colour and structure descriptor (HCSD). Finally, various classifiers are used for classification of images into normal and glaucomatous classes. The overall classification accuracy of HCSD with Hybrid Radial Basis Kernel based Support vector Machine (HRKSVM) is 97.55%, HCSD with Support vector Machine (SVM) is 94.77% and HCSD with Hybrid Kernel Support Vector Machine (HKSVM) is 95.71%.