Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model

被引：23

作者：

Liu, Tingting ^{[1
]}

Xu, Haiyong ^{[2
]}

Jin, Wei ^{[1
]}

Liu, Zhen ^{[1
]}

Zhao, Yiming ^{[2
]}

Tian, Wenzhe ^{[1
]}

机构：

[1] Ningbo Univ, Coll Informat Sci & Engn, Ningbo 315211, Zhejiang, Peoples R China

[2] Ningbo Univ, Coll Sci & Technol, Ningbo 315211, Zhejiang, Peoples R China

来源：

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE | 2014年 / 2014卷

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

SCALABLE FITTING ENERGY; LOCAL SEGMENTATION; DRIVEN; MINIMIZATION; MUMFORD; SPEED;

D O I：

10.1155/2014/890725

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.

引用

页数：10

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