Priori shape modeling method based on combined sparse model

Abstract

The invention discloses a priori shape modeling method based on a combined sparse model, and belongs to the technical field of medical image segmentation. Through priori shape modeling, a shape library obtained by collecting clinical data is established in allusion to a specific tissue organ, wherein the shape library consists of segmented shapes of image data from different patients, so as to establish a golden standard for corresponding organs of the patients. The priori shape modeling method based on a combined sparse model comprises the following steps: 1, gridding the surfaces of the shapes in advance by sampling points on the golden standard surfaces, wherein gridded shapes in the shape library are taken as training data of a model; 2, representing the gridded shapes by a sparse shape combining model, wherein coordinates of each shape in the shape library corresponding to all vertexes of a grid are arrayed as a column vector and an array D is obtained from the whole shape library; 3, performing optimization algorithm on the sparse shape combining model to obtain a corresponding parameter; and 4, performing inverse transformation on the corresponding parameter obtained in the optimization algorithm to obtain a required priori shape.

Description

Technical field [0001] The prior shape modeling method based on combined sparse model of the present invention belongs to the technical field of medical image segmentation, and relates to a prior shape modeling method in medical image segmentation, especially organs with individual adaptability in three-dimensional medical image segmentation The method of obtaining the prior shape. Background technique [0002] Medical image segmentation is a very challenging subject. Due to the low signal-to-noise ratio, low contrast, blurred boundaries between different soft tissues, sampling artifacts, local volume effects, spatial aliasing and other factors, there are many uncertain interferences in medical images, so medical image segmentation has a strong target. Due to the nature of medical image segmentation, there are no unified standards and universally applicable rules for medical image segmentation. [0003] Medical image segmentation has undergone a process of developing from traditi...

Images