Segmentation of medical images of different modalities using distance weighted C-V model

Authors:
Xiaozheng Liu;Wei Liu;Yan Xu;Yongdi Zhou;Junming Zhu;Bradley S. Peterson;Dongrong Xu
Affiliations:
Key Laboratory of Brain Functional Genomics, Ministry of Education, China & East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China and Columbia University ...;Key Laboratory of Brain Functional Genomics, Ministry of Education, China & East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China;Key Laboratory of Brain Functional Genomics, Ministry of Education, China & East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China;Department of Neurosurgery, Johns Hopkins University, Baltimore, MD;Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China;Columbia University, Dept. of Psychiatry & New York State Psychiatric Institute, New York, NY;Columbia University, Dept. of Psychiatry & New York State Psychiatric Institute, New York, NY
Venue:
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Year:
2011

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Abstract

Region-based active contour model (ACM) has been extensively used in medical image segmentation and Chan & Vese's (C-V) model is one of the most popular ACM methods. We propose to incorporate into the C-V model a weighting function to take into consideration the fact that different locations in an image with differing distances from the active contour have differing importance in generating the segmentation result, thereby making it a weighted C-V (WC-V) model. The theoretical properties of the model and our experiments both demonstrate that the proposed WC-V model can significantly reduce the computational cost while improve the accuracy of segmentation over the results using the C-V model.