PICE: prior information constrained evolution for 3-D and 4-D brain tumor segmentation

Authors:
Xiaojun Xue;Zhong Xue;Fei Cao;Ying Zhu;Geoffrey S. Young;Yan Li;Jianhua Yang;Stephen T. C. Wong
Affiliations:
The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, Texas and Schoo ...;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, Texas;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, Texas;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, Texas;Department of Radiology, Brigham and Women's Hospital, Harvard Medical School;School of Automation, Northwestern Polytechnical University, China;School of Automation, Northwestern Polytechnical University, China;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, Texas
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for Tlweighted MR brain images based on an improved level set method using prior information as a constraint, called Prior Infonnation Constrained Evolution (PICE). A new energy function in PICE incorporating the tumor intensity prior is designed to match brain tumor more accurately. The advantage of PICE has been illustrated by comparing with the traditional level set method in 3-D. In addition, we also illustrate that PICE can be easily applied to 4-D images, which facilitates follow-up studies of brain tumor treatments. Using longitudinal GBM data from five patients we showed the advantages of the proposed algorithm.