Bounded Rayleigh mixture model for ultrasound image segmentation

H. Bi; H. Tang; H. Z. Shu; J. L. Dillenseger

doi:10.1117/12.2266963

8 February 2017 Bounded Rayleigh mixture model for ultrasound image segmentation

H. Bi, H. Tang, H. Z. Shu, J. L. Dillenseger

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022514 (2017) https://doi.org/10.1117/12.2266963
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

The finite mixture model based on the Gaussian distribution is a flexible and powerful tool to address image segmentation. However, in the case of ultrasound images, the intensity distributions are non-symmetric whereas the Gaussian distribution is symmetric. In this study, a new finite bounded Rayleigh distribution is proposed. One advantage of the proposed model is that Rayleigh distribution is non-symmetric which has ability to fit the shape of medical ultrasound data. Another advantage is that each component of the proposed model is suitable for the ultrasound image segmentation. We also apply the bounded Rayleigh mixture model in order to improve the accuracy and to reduce the computational time. Experiments show that the proposed model outperforms the state-of-art methods on time consumption and accuracy.

Citation Download Citation

H. Bi, H. Tang, H. Z. Shu, and J. L. Dillenseger "Bounded Rayleigh mixture model for ultrasound image segmentation", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022514 (8 February 2017); https://doi.org/10.1117/12.2266963

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