A Nonlinear Adaptive Level Set for Image Segmentation

被引：83
|
作者：
Wang, Bin [1 ]
Gao, Xinbo [1 ]
Tao, Dacheng [2 ]
Li, Xuelong [3 ]
机构：
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China
[2] Univ Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, Ultimo 2007, Australia
[3] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, NSW, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2014年 / 44卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Active contour; Bayesian criterion; finite difference; image segmentation; level set; partial differential equation; ACTIVE CONTOURS; EVOLUTION; MUMFORD; MODEL;
D O I：
10.1109/TCYB.2013.2256891
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we present a novel level set method (LSM) for image segmentation. By utilizing the Bayesian rule, we design a nonlinear adaptive velocity and a probability-weighted stopping force to implement a robust segmentation for objects with weak boundaries. The proposed method is featured by the following three properties: 1) it automatically determines the curve to shrink or expand by utilizing the Bayesian rule to involve the regional features of images; 2) it drives the curve evolve with an appropriate speed to avoid the leakage at weak boundaries; and 3) it reduces the influence of false boundaries, i.e., edges far away from objects of interest. We applied the proposed segmentation method to artificial images, medical images and the BSD-300 image dataset for qualitative and quantitative evaluations. The comparison results show the proposed method performs competitively, compared with the LSM and its representative variants.
引用
收藏
页码：418 / 428
页数：11
相关论文
共 50 条
  • [21] An adaptive level set segmentation on a triangulated mesh
    Xu, MH
    Thompson, PM
    Toga, AW
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2004, 23 (02) : 191 - 201
  • [22] A Novel Adaptive Level Set Segmentation Method
    Lin, Yazhong
    Zheng, Qian
    Chen, Jiaqiang
    Cai, Qian
    Feng, Qianjin
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2014, 2014
  • [23] Integrating Adaptive Probabilistic Neural Network With Level Set Methods For MR Image Segmentation
    Lian, Yuanfeng
    Wu, Falin
    2011 6TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2011, : 1746 - 1749
  • [24] Image Segmentation Method for Complex Vehicle Lights Based on Adaptive Significance Level Set
    Jia Dongyao
    Zhu Huaihua
    Ai Yanke
    Zou Shengxiong
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2014, 103 (06): : 411 - 427
  • [25] ADAPTIVE REGULARIZATION LEVEL SET EVOLUTION FOR MEDICAL IMAGE SEGMENTATION AND BIAS FIELD CORRECTION
    Xin, Xiaomeng
    Wang, Lingfeng
    Pan, Chunhong
    Liu, Shigang
    2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, : 1006 - 1010
  • [26] Image Segmentation With Adaptive Edge-Region Collaborated Level-Set Method
    Zhang, Weihang
    Li, Huiqi
    IEEE SIGNAL PROCESSING LETTERS, 2024, 31 : 2105 - 2109
  • [27] Particle Analysis Using Improved Adaptive Level Set Method Based Image Segmentation
    Sarkhawas, Guzayya
    Bang, Arti
    Dandawate, Yogesh
    1ST INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION ICCUBEA 2015, 2015, : 747 - 751
  • [28] CULTURAL ALGORITHM FOR LEVEL SET IMAGE SEGMENTATION
    Dong, Guanghui
    Xi, Zhihong
    Xiao, Yihan
    3RD INTERNATIONAL SYMPOSIUM ON INFORMATION ENGINEERING AND ELECTRONIC COMMERCE (IEEC 2011), PROCEEDINGS, 2011, : 51 - 54
  • [29] A Deep Level Set Method for Image Segmentation
    Tang, Min
    Valipour, Sepehr
    Zhang, Zichen
    Cobzas, Dana
    Jagersand, Martin
    DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, 2017, 10553 : 126 - 134
  • [30] Image segmentation based on level set method
    Ouyang Yimin
    Qi Xiaoping
    Zhang Qiheng
    ELECTRO-OPTICAL AND INFRARED SYSTEMS: TECHNOLOGY AND APPLICATIONS IV, 2007, 6737
12345