Earticle

초록

영어

Delineating brain tumor boundaries from multi-modality magnetic resonance images (MRIs) is a crucial step in brain cancer surgical and treatment planning. In this paper, we propose a fully automatic technique for brain tumor segmentation from multi-modality human brain MRIs. We first use the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues. Then, the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image. At last, the spatial-contextual information is proposed by constraining the classified neighbors to improve the classification accuracy. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.

목차

Abstract
 1. Introduction
 2. Multiple Classifier System
  2.1. Naïve Bayes Classifier
  2.2. Multinomial Logistic Regression Classifier
  2.3. Bayes Weighted Average
 3. Global Energy Cost Function Imposing the Spatial Constraint
 4. Results and Discussion
 5. Conclusions
 References

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 ]
• Lei Jiang [ Petrochina Southwest Oil &Gasfield Company, Luzhou 646000, China ]
• Yongzhao Zhan [ School of Computer Science & Communications Engineering, Jiangsu University, Zhenjiang 212013, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605