원문정보
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
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