Classification and Segmentation of Glaucomatous Image Using Probabilistic Neural Network (PNN), K-Means and Fuzzy C-Means(FCM)

The gradual visual field loss and there is a characteristic type of damage to the retinal nerve fiber layer associated with the progression of the disease glaucoma. Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subband is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the Daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. Here my project aims at the use of Probabilistic Neural Network (PNN), Fuzzy C-means (FCM) and K-means helps for the detection of glaucoma disease. For this, fuzzy c-means clustering algorithm and k-means algorithm is used. Fuzzy c-means results faster and reliably good clustering when compare to k-means.