Abstract
Multiresolution-based magnetic resonance (MR) image segmentation has attracted attention for its ability to capture rich information across scales compared with the conventional segmentation methods. In this paper, a new scale-space-based segmentation model is presented, where both the intra-scale and inter-scale properties are considered and formulated as two fuzzy energy functions. Meanwhile, a control parameter is introduced to adjust the contribution of the similarity character across scales and the clustering character within the scale. By minimizing the combined inter/intra energy function, the multiresolution fuzzy segmentation algorithm is derived. Then the coarse to fine leading segmentation is performed automatically and iteratively on a set of multiresolution images. The validity of the proposed algorithm is demonstrated by the test image and pathological MR images. Experiments show that by this approach the segmentation results, especially in the tumor area delineation, are more precise than those of the conventional fuzzy segmentation methods.
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Supported by the National Natural Science Foundation of China under Grant Nos.60071029, 60271022 and the Creative Research Group Science Foundation of China under Grant No.60024301.
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Zhang, H., Bian, Z., Yuan, Z. et al. A novel multiresolution fuzzy segmentation method on MR image. J. Comput. Sci. & Technol. 18, 659–666 (2003). https://doi.org/10.1007/BF02947126
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DOI: https://doi.org/10.1007/BF02947126