A Method of Segmentation For Glaucoma Screening Using Superpixel Classification
Glaucoma is a chronic eye disease that leads to vision loss. Detecting glaucoma in time is critical. The symptoms of the glaucoma disease occur when it is in advanced stage. Hence, glaucoma is called the silent thief of sight. Detecting the disease in time is very important. In this project, an optic disc and optic cup segmentation is used to identify the glaucoma disease in time. In optic disc and optic cup segmentation, super pixel classification for glaucoma screening is proposed. In optic disc segmentation, histograms and centre surround statistics are used to classify each super pixel as disc or non-disc. A self assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. In optic cup segmentation, the location information is also included into the feature space for better performance in addition to the histograms and centre surround statistics. The segmented optic cup and optic disc is then used to compute the cup to disc ratio for glaucoma screening. From the cup to disc ratio, analysis is performed to identify whether the given image is glaucomatous or not. The segmentation can be analyzed using the MATLAB.
Eleesa Jacob, R.Venkatesh
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