A robust patch-statistical active contour model for image segmentation

Authors:
Qi Ge;Liang Xiao;Jun Zhang;Zhi Hui Wei
Affiliations:
School of Computer Science, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China;School of Computer Science, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China;School of Science, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China;School of Computer Science, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

This paper proposes a novel region-based active contour model (ACM) for image segmentation, which is robust to noise and intensity non-uniformity. The energy functional of the proposed model consists of three terms, i.e., the patch-statistical region fitting term, the improved regularization term, and the intensity variation penalization term. The patch-statistical region fitting term computes the local statistical information in each patch as the basis for driving the curve accurately with resist to intensity non-uniformity and weak boundaries. And the regularization term coupling with the gradient information improves the ability of capturing the boundaries with cusps and narrow topology structures. Furthermore, an intensity variation penalization term is proposed to make sure that the segmentation result is robust to the irregular intensity variation. Experiments on medical and natural images show that the proposed model is more robust than the popular active contour models for image segmentation with noise and intensity non-uniformity.