Abstract
The segmentation algorithms based on MRF often exist edge block effect, and have low operation efficiency by modeling the whole image. To solve the problems the image segmentation algorithm using edge multiscale domain hierarchical Markov model is presented. It views an edge as an observable data series, the image characteristic field is built on a series of edge extracted by wavelet transform, and the label field MRF model based on the edge is established to integrate the scale interaction in the model, then the image segmentation is obtained. The test images and medical images are experimented, and the results show that compared with the WMSRF algorithm, the proposed algorithm can not only distinguish effectively different regions, but also retain the edge information very well, and improve the efficiency. Both the visual effects and evaluation parameters illustrate the effectiveness of the proposed algorithm.
Project supported by the National Nature Science Foundation of China( Nos 61020106001, 60933008, 60903109, 61170161), the Nature Science Foundation of Shandong Province(Nos.ZR2011G0001, ZR2012FQ029) , and Nature Science Foundation of Ludong University(No. LY2010014)
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Tang, W., Zhang, C., Zou, H. (2013). Edge Multi-scale Markov Random Field Model Based Medical Image Segmentation in Wavelet Domain. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_7
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DOI: https://doi.org/10.1007/978-3-642-39482-9_7
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