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A novel multiphase active contour model for inhomogeneous image segmentation

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Abstract

The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images segmentation. The complex images usually contain an arbitrary number of objects. This paper presents a new multiphase active contour model method for simultaneous regions classification of MR images and natural images without bias field correction. In this model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the multiple regions. This model not only extracts multiple objects simultaneously, but also provides smooth and accurate boundaries of the objects. The results for experiments on several synthetic and real images demonstrate the effectiveness and accuracy of our model.

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Acknowledgments

This work was supported by the Qing Lan project of Jiang Su, the Major Program of the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (11KJA460001), Jiangsu 333 Project, Huai’an 533 Project and supported in part by the Major Program for scientific and technological research in University of China under the Grant No.311024.

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Authors and Affiliations

  1. The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Shangbing Gao, Jian Yang & Yunyang Yan

  2. Faculty of Computer Engineering, Huaiyin Institute of Technology, Huai’an, 223003, People’s Republic of China

    Shangbing Gao & Yunyang Yan

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  1. Shangbing Gao
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  2. Jian Yang
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  3. Yunyang Yan
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Correspondence to Shangbing Gao.

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Gao, S., Yang, J. & Yan, Y. A novel multiphase active contour model for inhomogeneous image segmentation. Multimed Tools Appl 72, 2321–2337 (2014). https://doi.org/10.1007/s11042-013-1553-2

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  • DOI: https://doi.org/10.1007/s11042-013-1553-2

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