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Image segmentation based on multi-region multi-scale local binary fitting and Kullback–Leibler divergence

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Abstract

The inhomogeneity of intensity and the noise of image are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges and address the difficulty of image segmentation methods to handle an arbitrary number of regions, we propose a region-based multi-phase level set method, which is based on the multi-scale local binary fitting (MLBF) and the Kullback–Leibler (KL) divergence, called KL–MMLBF. We first apply the multi-scale theory and multi-phase level set framework to the local binary fitting model to build the multi-region multi-scale local binary fitting (MMLBF). Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MMLBF. KL–MMLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL–MMLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and medical images have shown that KL–MMLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating this minimization of this energy function and the model has achieved better segmentation results in terms of both accuracy and efficiency to analyze the multi-region image.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 61402133).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin, 150001, People’s Republic of China

    Dansong Cheng, Lin Liu & Ye Jin

  2. School of Electrical Engineering and Automation, Harbin Institute Technology, Harbin, People’s Republic of China

    Xiaofang Liu

  3. Faculty of Science and Technology, Bournemouth University, Poole, UK

    Feng Tian

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  1. Dansong Cheng
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  2. Feng Tian
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  3. Lin Liu
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  4. Xiaofang Liu
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  5. Ye Jin
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Correspondence to Xiaofang Liu.

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Cheng, D., Tian, F., Liu, L. et al. Image segmentation based on multi-region multi-scale local binary fitting and Kullback–Leibler divergence. SIViP 12, 895–903 (2018). https://doi.org/10.1007/s11760-017-1234-0

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  • DOI: https://doi.org/10.1007/s11760-017-1234-0

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