Affiliation: | (1) School of Life Science and Technology, Xi'an Jiaotong University, 710049 Xi'an, P.R. China;(2) Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, 710049 Xi'an, P.R. China;(3) Construction Machinery College, Chang'an University, 710064 Xi'an, P.R. China;(4) Shaanxi Promotion Center of Production, 710054 Xi'an, P.R. China |
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. |