Continuous Level Set Algorithm Based on Improved CV Model for Magnetic Resonance Breast Image Segmentation

doi:10.7652/xjtuxb201402007

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Continuous Level Set Algorithm Based on Improved CV Model for Magnetic Resonance Breast Image Segmentation

更新时间：2025-07-09

- Continuous Level Set Algorithm Based on Improved CV Model for Magnetic Resonance Breast Image Segmentation
- Vol. 48, Issue 2, Pages: 38-43(2014)
- 作者机构：
  
  陕西师范大学计算机科学学院,西安,710062
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201402007
  CLC： TP391.72
- Online First：10 February 2014，
  
  Published：2014
- 稿件说明：
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Continuous Level Set Algorithm Based on Improved CV Model for Magnetic Resonance Breast Image Segmentation[J]. 2014, 48(2): 38-43.
DOI：

Continuous Level Set Algorithm Based on Improved CV Model for Magnetic Resonance Breast Image Segmentation[J]. 2014, 48(2): 38-43. DOI： 10.7652/xjtuxb201402007.

摘要

针对核磁共振乳腺图像边界弱、信息量大、信噪比低的问题

提出一种基于改进Chan-Vese(CV)模型的连续水平集分割算法。该算法利用B样条基函数将传统离散水平集函数表示成连续形式

用解决B样条空间的变分问题代替水平集函数更新的计算问题; 通过引入转移Heaviside函数

构造α-CV模型作为能量函数模型。实验结果表明

与传统CV模型离散水平集方法相比

该算法可以避免局部极小值的现象

提高分割精度

有效抑制噪声

分割迭代次数降低了10

数量级

并且可以准确、稳定地实现低信噪比、弱边界的核磁共振乳腺图像分割。

Abstract

A continuous level set algorithm based on improved Chan-Vese(CV)model is proposed to segment magnetic resonance(MR)breast images with weak edges

large information

and low signal-to-noise ratio. B-spline basis functions are used to express the traditional discrete level set function as a continuous level set function

and the re-initialization of the level set function is replaced by the solution of variational problem in B-spline space. An α-CV model is constructed to form energy function by introducing a shifted Heaviside function. Experimental results and comparison with the traditional CV model method show that the proposed algorithm is able to avoid local minimum

to improve segmentation accuracy

and to suppress noise effectively with a reduction in the number of algorithm iterations by magnitude of 10

关键词

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references

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