Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 44, Issue 3, Pages 418-428Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2013.2256891
Keywords
Active contour; Bayesian criterion; finite difference; image segmentation; level set; partial differential equation
Categories
Funding
- National Basic Research Program of China [2012CB316400]
- National Natural Science Foundation of China [61125204, 61125106, 61201293, 41031064, 61172146, 91120302, 61072093]
- Fundamental Research Funds for the Central Universities [K5051202048, K50511020016]
- Research for the Doctoral Program of Higher Education of China [20120203120012]
- China Post-Doctoral Science Foundation [20110490166]
- Ocean Public Welfare Scientific Research Project, State Oceanic Administration People's Republic of China [201005017]
- Shaanxi Innovative Research Team for Key Science and Technology [2012KCT-02]
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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.
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