Earticle

초록

영어

Automatic brain tumor segmentation from multispectral magnetic resonance imaging (MRI) data is an important but a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. In this paper, we propose a fully automatic technique for brain tumor segmentation from multispectral human brain MRIs. We first use the intensities of different patches in multispectral MRIs to represent the features of both normal and abnormal tissues and generate a dictionary for following tissue classification. Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel intensities. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.

목차

Abstract
 1. Introduction
 2. Sparse Representation Classifier
 3. Our Method
 4. Implementation and Results
 5. Conclusion
 Acknowledgements
 6. References
  6.1. Journal Articles
  6.2. Conference Proceedings

키워드

저자

• Tianming Zhan [ School of Computer Science & communications Engineering, Jiangsu University, Zhenjiang 212013, China ]
• Shenghua Gu [ Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China. ]
• Can Feng [ Center of software, China North Industries Group Corporation Co., Ltd., Nanjing, 210044, China ]
• Yongzhao Zhan [ School of Computer Science & communications Engineering, Jiangsu University, Zhenjiang 212013, China ]
• Jin Wang [ Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605