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In the context of biomedical imaging analysis and computer-assisted diagnosis, segmentation analysis is an intense field of research and development. The most difficult part of medical image analysis is the automated localization and delineation of structures of interest. Automated data evaluation is one way of enhancing the clinical utility of measurements. In particular, medical image segmentation extracts meaningful information and facilitate the display of this information in a clinically relevant way. A crucial role for automated information extraction in medical imaging usually involves the segmentation of regions of the image in order to quantify volumes and areas of interest of biological tissues for further diagnosis and localization of pathologies. Optical coherence tomography (OCT) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states and molecular content (Huang et al., 1991). OCT uses the principle of low coherence interferometry to generate two or three dimensional imaging of biological samples by obtaining high-resolution cross-sectional backscattering profiles. A variety of successful algorithms for computer-aided diagnosis by means of OCT image analysis are presented in the literature, but robust use in clinical practice is still a major challenge for ongoing research in OCT image analysis. There are, therefore, efforts being made to improve clinical decision making based on automated analysis of OCT data. Particularly, in ophthalmology, efforts have been made to characterize clinically important features, such as damage to the fovea and optic nerve, automatically. The transfer of image analysis models from algorithmic development into clinical application is currently the major bottleneck due to the complexity of the overall process. For example, the process to establish an application for OCT medical image analysis requires difficult and complex tasks that should considers the following actions: 1) to define the OCT image data structures representing relevant biomedical features and the algorithms determining a valid example for given image values, 2) to select meaningful values for all technical parameters of the image data structures and algorithms and, as a result, to configure such a method to operate on specific OCT clinical data, 3) to run the algorithm with the selected parameters to find the individual model instance that best explains the input image and 4) to validate the procedure to ensure a trustworthy result from an automated segmentation algorithm even if a gold standard is unavailable.

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography