Image processing method for ophthalmic optical coherence tomography

Optical coherence tomography (OCT) has become a hot research topic in the field of clinical medicine due to its features including micron-level high resolution, non-invasive imaging and instantaneity, which has developed rapidly and made much progress and break throughs in recent years. In this paper we briefly review the applications of OCT in ophthalmology, discuss the methods of speckle noise reduction in the spatial and frequency domains of OCT images, and summarize the precise positioning and stratification method of each layer of tissue in the OCT anterior segment and retina image. The advantages and disadvantages of the segmentation methods based on gray value search, active contour model, graph and pattern recognition algorithms are analyzed and compared. In addition, the existing problems with segmentation methods are discussed and the corresponding solutions and feasible optimization schemes are proposed. Analysis and evaluation of clinical diagnostic indicators of ophthalmic diseases are discussed. According to the needs in ophthalmology and the current status of OCT image processing, the development trends and level of OCT image processing are discussed and analyzed.