ACTIVE CONTOUR- AND DEEP LEARNING-BASED AUTOMATIC SEGMENTATION METHOD FOR FUZZY BOUNDARY IMAGE

摘要

Disclosed in the present invention is an active contour- and deep learning-based automatic segmentation method for a fuzzy boundary image. In the method, first, a deep convolutional neural network model is used to segment a fuzzy boundary image to obtain an initial segmentation resu then, a contour in an inner region of the image segmented by the deep convolutional neural network model is used as an initialized contour and a contour constraint for an active contour model; and the active contour model, by means of an image feature of a surrounding region of each contour point, a contour to move towards a target boundary, and a precise segmentation line is obtained between a target region and other background regions. In the present invention, an active contour model is added to a base of a deep convolutional neural network model to further refine a segmentation result for a fuzzy boundary image, thus having the ability to segment a fuzzy boundary in an image, and further improving segmentation accuracy in a fuzzy boundary image.