公开(公告)号：US12073564B2

公开(公告)日：2024-08-27

申请号：US17641445

申请日：2020-10-31

Applicant: SOUTH CHINA UNIVERSITY OF TECHNOLOGY

Inventor： Junying Chen ,  Haijun You

IPC: G06K9/00 ,  A61K35/12 ,  G06T7/11 ,  G06T7/12 ,  G06T7/149 ,  G06T7/62

CPC classification number: G06T7/149 ,  G06T7/11 ,  G06T7/12 ,  G06T7/62 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20116 ,  G06T2207/20161 ,  G06T2207/30004

Abstract: The present invention discloses a method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning. In the method, firstly, a fuzzy boundary image is segmented using a deep convolutional neural network model to obtain an initial segmentation result; then, a contour of a region inside the image segmented using the deep convolutional neural network model is used as an initialized contour and a contour constraint of an active contour model; and the active contour model drives, through image characteristics of a surrounding region of each contour point, the contour to move towards a target edge to derive an accurate segmentation line between a target region and other background regions. The present invention introduces an active contour model on the basis of a deep convolutional neural network model to further refine a segmentation result of a fuzzy boundary image, which has the capability of segmenting a fuzzy boundary in the image, thus further improving the accuracy of segmentation of the fuzzy boundary image.