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Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform

Optical Coherence Tomography (OCT) has emerged as a major diagnostic modality for retinal imaging. Although OCT generates gross volumetric data, manual analysis of the images for locating or quantifying retinal cysts is a time consuming process. Recently semi- and fully-automatic methods for locating and segmenting retinal cysts have been proposed in the literature. Our paper proposes a fully automatic method for intra-retinal cyst segmentation using marker controlled watershed transform on B-scan images obtained on OCT scanning. Markers are obtained using k-means clustering and used as sources for topographical based watershed transform for final segmentation. Proposed method was evaluated both quantitatively and qualitatively on Optima Cyst Challenge dataset against ground truth obtained from two graders. Experimental results show that the proposed method outperformed other recently proposed methods. Our algorithm achieved a recall rate of 82% while preserving precision rate of 77%, and gave a higher correlation rate of 96% with ground truth obtained from two graders.