Abstract: The present invention discloses a method and device for quick segmentation of an optical coherence tomography image, a computing device, and a computer readable storage medium. The method includes: extracting a feature value related to a pixel in the optical coherence tomography image from the pixel to obtain a feature value representative of the pixel feature, the feature value including at least one of an intensity gradient value of the pixel, an intensity multi-scale mean value of the pixel, and an angular feature value extracted from the pixel; inputting the feature value into a Sparse Bayesian Classification model containing values related to image features corresponding to a border, to obtain a probability that the image feature belongs to features of various image region borders; and obtaining borders of various image regions contained in a predefined image region based on the probabilities obtained for various pixels in the predefined image region.

Abstract: The present invention discloses a method and device for quick segmentation of an optical coherence tomography image, a computing device, and a computer readable storage medium. The method includes: extracting a feature value related to a pixel in the optical coherence tomography image from the pixel to obtain a feature value representative of the pixel feature, the feature value including at least one of an intensity gradient value of the pixel, an intensity multi-scale mean value of the pixel, and an angular feature value extracted from the pixel; inputting the feature value into a Sparse Bayesian Classification model containing values related to image features corresponding to a border, to obtain a probability that the image feature belongs to features of various image region borders; and obtaining borders of various image regions contained in a predefined image region based on the probabilities obtained for various pixels in the predefined image region.