Abstract
In this paper, we proposed an improved ultrasound image segmentation algorithm for cattle follicle based on Markov random field model. According to the original ultrasound image dataset, we removed the speckle noise in ultrasound images by anisotropic diffusion filtering algorithm on the first step, and used the image enhancement technology to enhance the contrast of target area, then combined with an improved k-means algorithm for initial segmentation to realize basic classification of image pixels. As for the discontinuous over segmentation, we used area rule to remove the discontinuous over-segmentation region. Compared to the traditional MRF algorithm, this new algorithm has more accurate segmentation of the target area, better segmentation effect. The improved k-means algorithm to make initial segmentation for MRF model can also avoid initial clustering center to be selected randomly in comparison with the traditional k-means algorithm.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 31201121, No. 61373109 and No. 61403287), the Natural Science Foundation of Hubei Province (Grant No. 2014CFB288) and Open foundation of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (Grant Nos. ZNSS2013A001 and ZNSS2013A004).
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Liu, J., Guan, B. (2016). An Improved Ultrasound Image Segmentation Algorithm for Cattle Follicle Based on Markov Random Field Model. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_55
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DOI: https://doi.org/10.1007/978-3-319-42294-7_55
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