Glaucoma diagnosis using discrete wavelet transform and histogram features from fundus images

Glaucoma is one of the main eye diseases; it cause progressive deterioration of optic nerve fibers due to increased fluid pressure. The existing methods of glaucoma diagnosis are time consuming, expensive and require practiced clinicians to understand the eye problems. Hence fast, cheap and more accurate glaucoma diagnosis methods are needed. This paper presents an innovative idea for diagnosis of glaucoma using third level two dimensional discrete wavelet transform (2D DWT) and histogram features from fundus images. The 2D DWT is used to decompose the glaucoma and healthy images and histogram features are extracted from 2D DWT decomposed sub band images. The least square support vector machine (LS-SVM) is used as a classifier which classifies the glaucoma and healthy images using the extracted features. The proposed method yielded classification accuracy of 88.33%, 87.50%, and 86.67% for ten, eight and fivefold cross validation respectively. The obtained classification accuracy, sensitivity and specificity are 88.33%, 90.00%, and 85.00% for tenfold cross validation respectively. Obtained results prove that the performance of the proposed method is better compared to the existing methods. It may considerably increases the diagnosis speed of ophthalmologists.

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