Chapter 10 - State-of-the-Art of Level-Set Methods in Segmentation and Registration of Spectral Domain Optical Coherence Tomographic Retinal Images

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In the quantitative assessment of Diabetic maculopathy from Spectral Domain Optical Coherence Tomography (SDOCT) images, analysis of intraretinal fluid filled regions plays a vital role because of its comparative superiority in providing tissue-level anatomical information. The detailed study on efficacy and performance of soft computing techniques-based automatic detection and diagnosis for SDOCT retinal images is still in the preliminary stage. Although some automatic algorithms have been proposed to segment retinal layers in recent times, full accuracy in edge detection continues to be a challenging problem. Some researchers have developed different versions of automatic algorithms for segmenting intraretinal fluid based on region-based level-set method and the retinal layers by dual gradient method. This particular level-set implementation is carried out using a fast front propagation algorithm. A valid search region is then defined to identify layer boundaries. The features of the segmented region are analyzed volumetrically and based on these temporal characteristics, the severity of the disease can then be estimated. Some operational algorithms have also been developed for registration of Peripapillary OCT and fundus image for the identification of Neovascularization in the early stage of diabetic proliferative retinopathy.