A two-stage image segmentation method using Euler's Elastica Regularized Mumford-Shah model

Yuping Duan, Weimin Huang, Jiayin Zhou, Huibin Chang, Tieyong ZENG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

As one of the most important image segmentation models, the Mumford-Shah functional was developed to pursue a piecewise smooth approximation of a given image based on the regularization on the total length of curves. In this paper, we modify the Mumford-Shah model using Euler's elastic a as the regularization. A two-stage segmentation method is applied the Euler's elastic a regularized Mumford-Shah model. The first stage is to find a smooth solution of the variant Mumford-Shah functional based on augmented Lagrangian method while a thresholding is performed in the second stage to obtain different phases for the segmentation. The K-means clustering method is used as the technique to find the thresholds for the segmentation. For intensity inhomogeneous images, we eliminate the effect of the bias field by bias-corrected fuzzy c-means method. Experimental results show that as the regularization, Euler's elastic a makes the Mumford-Shah model perform better for many kinds of images, including tubular and irregular shaped, CT Angiography (CTA) and MRI images in different noise level.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-123
Number of pages6
ISBN (Electronic)9781479952083
DOIs
Publication statusPublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period24/08/1428/08/14

Scopus Subject Areas

  • Computer Vision and Pattern Recognition

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