|
|
Brain tumor extraction based on spatial-feature combining kernel sparse representation |
1. College of Computer Science and Engineering, Sanjiang University, Nanjing, Jiangsu 210012, China; 2. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210000, China; 3. School of Computer Science and Communication Enginee-ring, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
|
|
Abstract To improve the brain tumor extraction accuracy by multisequence MR image, the spatial-feature combining kernel sparse representation (KSR) was proposed to connect spatial structure information and intensity feature information for brain tumor extraction. The sub-dictionary of each label was built, and the neighboring filtering kernel based on KSR was applied to extract brain tumor from MSMR images. The spatial information and the intensity feature information were combined in the proposed method to improve the accuracy of brain tumor extraction. The clinical and simulation data from MICCAI BraTS dataset were divided by the proposed method. The results show that compared with sparse representation method, the proposed brain tumor extraction method based on spatial-feature kernel sparse representation can increase the brain tumor extraction accuracy by 5%~6% due to the introducing of spatial structure information.
|
Received: 07 August 2016
|
|
|
|
|
|
|