earticle

논문검색

A Novel Brain Tumor Segmentation Method for Multi-Modality Human Brain MRIs

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

저자정보

  • 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

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.