Incremental variation level set fast medical image partition method
A medical image, level set technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as inability to use and improve segmentation efficiency
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2008-12-03
Smart Images
Figure 1 Figure 2 Figure 3
Abstract
Description
(1) Technical field
[0001] The invention relates to the field of medical image segmentation, in particular to a fast medical image segmentation method. (2) Background technology
[0002] In medical image processing and analysis applications, image segmentation technology plays a key role. The task of medical image segmentation is to extract regions of interest containing important diagnostic information from medical images, and provide a reliable basis for clinical diagnosis and pathology research. Due to the complexity and difference of the imaging principle of medical images and the structure of human tissue itself, medical images inevitably have the characteristics of blur and inhomogeneity compared with ordinary images; at the same time, the rapid development of medical imaging technology makes it possible to acquire various Massive medical image data become possible, all of which put forward higher requirements for image segmentation technology.
[0003] In recent yea...
Examples
Embodiment Construction
[0058] Below in conjunction with accompanying drawing, the present invention will be further described:
[0059] The incremental variational level set rapid medical image segmentation method proposed in the present invention is mainly based on the formula (9) for numerical calculation, and is simple to implement. The specific implementation of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0060] Taking the two-phase segmentation problem of a two-dimensional image as an example, the original image is as figure 2 shown. First select the appropriate model parameters μ, v, λ according to the actual segmentation problem 1 ,λ 2 , respectively μ=1.0, v=0, λ in this embodiment 1 =λ 2 = 1.0, then refer to figure 1 Carry out the following segmentation process:
[0061] (1) Given the initial boundary C 0 , with this boundary, the area Ω can be obtained 1 0 ,Ω 2 0 , respectively calculate its average gray level...