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research-article

A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement

Authors:

Xiao-Feng Wang,

Yi-Gang ZhangAuthors Info & Claims

Pattern Recognition, Volume 48, Issue 1

Pages 189 - 204

https://doi.org/10.1016/j.patcog.2014.07.008

Published: 01 January 2015 Publication History

Abstract

This paper presents a novel level set method for complex image segmentation, where the local statistical analysis and global similarity measurement are both incorporated into the construction of energy functional. The intensity statistical analysis is performed on local circular regions centered in each pixel so that the local energy term is constructed in a piecewise constant way. Meanwhile, the Bhattacharyya coefficient is utilized to measure the similarity between probability distribution functions for intensities inside and outside the evolving contour. The global energy term can be formulated by minimizing the Bhattacharyya coefficient. To avoid the time-consuming re-initialization step, the penalty energy term associated with a new double-well potential is constructed to maintain the signed distance property of level set function. The experiments and comparisons with four popular models on synthetic and real images have demonstrated that our method is efficient and robust for segmenting noisy images, images with intensity inhomogeneity, texture images and multiphase images. The intensity statistical analysis is performed on local circular regions.The global energy term is formulated by minimizing the Bhattacharyya coefficient.The penalty energy term associated with a new double-well potential is proposed.The proposed method is efficient and robust for segmenting complex images.

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Cited By

Zhang FLiu HDuan XWang BCai QLi HDong JZhang D(2024)DSLSMExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122772242:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122772
Feng CChen SZhao DYang J(2023)Region based level sets for image segmentation: a brief comparative review with a fast model FREESTMultimedia Tools and Applications10.1007/s11042-023-15073-x82:24(37065-37095)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11042-023-15073-x
Soleymanifard MHamghalam M(2022)Multi-stage glioma segmentation for tumour grade classification based on multiscale fuzzy C-meansMultimedia Tools and Applications10.1007/s11042-022-12326-z81:6(8451-8470)Online publication date: 1-Mar-2022
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A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 48, Issue 1

January 2015

279 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2015

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Cited By

Zhang FLiu HDuan XWang BCai QLi HDong JZhang D(2024)DSLSMExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122772242:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122772
Feng CChen SZhao DYang J(2023)Region based level sets for image segmentation: a brief comparative review with a fast model FREESTMultimedia Tools and Applications10.1007/s11042-023-15073-x82:24(37065-37095)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s11042-023-15073-x
Soleymanifard MHamghalam M(2022)Multi-stage glioma segmentation for tumour grade classification based on multiscale fuzzy C-meansMultimedia Tools and Applications10.1007/s11042-022-12326-z81:6(8451-8470)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-022-12326-z
Chen JChen SHu X(2022)Image segmentation by phase-field models with local informationMultimedia Tools and Applications10.1007/s11042-021-11718-x81:2(3039-3057)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-11718-x
Liu JWang JLi QShi M(2019)A Novel Image Segmentation Algorithm Based on Active Contour Model and Retinex ModelProceedings of the 2019 3rd International Conference on Advances in Image Processing10.1145/3373419.3373451(60-64)Online publication date: 8-Nov-2019
https://dl.acm.org/doi/10.1145/3373419.3373451
Li YCao GYu QLi X(2019)Fast and Robust Active Contours Model for Image SegmentationNeural Processing Letters10.1007/s11063-018-9827-349:2(431-452)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11063-018-9827-3
Benaissa MBennia A(2019)Block classification based edge detector and object localizerMultimedia Tools and Applications10.1007/s11042-018-6821-878:11(14573-14589)Online publication date: 31-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-018-6821-8
Yu HHe FPan Y(2019)A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbationMultimedia Tools and Applications10.1007/s11042-018-6735-578:9(11779-11798)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6735-5
Zhang FZhang BZhang X(2019)Dislocation Theory Based Level Set Image SegmentationIntelligent Computing Theories and Application10.1007/978-3-030-26763-6_21(218-227)Online publication date: 3-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-26763-6_21
Min HJia WZhao Y(2018)A Region-Bias Fitting Model based Level Set for Segmenting Images with Intensity InhomogeneityProceedings of the 2nd International Symposium on Image Computing and Digital Medicine10.1145/3285996.3286015(83-87)Online publication date: 13-Oct-2018
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