Cup-disk segmentation and fractal dimensionto detect glaucomatous eyes

The present paper explores a state of art for detection of glaucoma in human eye through cup-disk segmentation & fractal dimension using image analysis. Author extracted fractal dimension feature from glaucomatous eyes for the portion of segmented cup-disk through semi variance algorithm. In the first step authors adopted Chan-Vese algorithm to extract region of interest (ROI) for which fractal dimension is determined. The strong findings of the present paper are (1) Semi variance method found to be more efficient. (2) Fractal dimension feature could be used as a diagnostics parameter for earlier detection of glaucoma. (3) The visualization of segmentation reveals that decrease in area of signifies attacking glaucoma neuroretinal rim by glaucoma. (4) Fractal Dimension (FD) found to be in the range of 1.50-159 for the glaucoma affected eye.