TY - GEN
T1 - Superpixel-based segmentation of glioblastoma multiforme from multimodal MR images
AU - Su, Po
AU - Yang, Jianhua
AU - Li, Hai
AU - Chi, Linda
AU - Xue, Zhong
AU - Wong, Stephen T.
PY - 2013
Y1 - 2013
N2 - Due to complex imaging characteristics such as large diversity in shapes and appearances combining with deformation of surrounding tissues, it is a challenging task to segment glioblastoma multiforme (GBM) from multimodal MR images. In particular, it is important to capture the heterogeneous features of enhanced tumor, necrosis, and non-enhancing T2 hyperintense regions (T2HI) to determine the aggressiveness of the tumor from neuroimaging. In this paper, we propose a superpixel-based graph spectral clustering method to improve the robustness of GBM segmentation. A new graph spectral clustering algorithm is designed to group superpixels to different tissue types. First, a local k-means clustering with weighted distances is employed to segment the MR images into a number of homogeneous regions, called superpixels. Then, the spectral clustering algorithm is utilized to extract the enhanced tumor, necrosis, and T2HI by considering the superpixel map as a graph. Experiment results demonstrate better performance of the proposed method by comparing with pixel-based and the normalized cut segmentation methods.
AB - Due to complex imaging characteristics such as large diversity in shapes and appearances combining with deformation of surrounding tissues, it is a challenging task to segment glioblastoma multiforme (GBM) from multimodal MR images. In particular, it is important to capture the heterogeneous features of enhanced tumor, necrosis, and non-enhancing T2 hyperintense regions (T2HI) to determine the aggressiveness of the tumor from neuroimaging. In this paper, we propose a superpixel-based graph spectral clustering method to improve the robustness of GBM segmentation. A new graph spectral clustering algorithm is designed to group superpixels to different tissue types. First, a local k-means clustering with weighted distances is employed to segment the MR images into a number of homogeneous regions, called superpixels. Then, the spectral clustering algorithm is utilized to extract the enhanced tumor, necrosis, and T2HI by considering the superpixel map as a graph. Experiment results demonstrate better performance of the proposed method by comparing with pixel-based and the normalized cut segmentation methods.
KW - GBM
KW - multimodal MR images
KW - spectral clustering
KW - superpixel
UR - http://www.scopus.com/inward/record.url?scp=84883294611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883294611&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02126-3_8
DO - 10.1007/978-3-319-02126-3_8
M3 - Conference contribution
AN - SCOPUS:84883294611
SN - 9783319021256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 74
EP - 83
BT - Multimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Proceedings
T2 - 3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -