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：
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
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
收藏
页数：10
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