Robust image segmentation via Bayesian type criterion

Xiaoguang Wang, Dawei Lu and Lixin Song

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 17, 4215-4228

Abstract: Image segmentation plays an important role in image processing before image recognition or compression. Many segmentation solutions follow the information theoretic criteria and often have excellent results; however, they are not robust to reduce the noise effect in contaminated image data. To guarantee the optimal segmentation with possible noise, a robust Bayesian information criterion is proposed to segment a grayscale image and it is less sensitive to noise. The asymptotic properties are also studied. Monte Carlo numerical experiments along with a brain magnetic resonance image are conducted to evaluate the performance of the new method.

Date: 2018
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DOI: 10.1080/03610926.2017.1371756

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