电子学报2017，Vol.45Issue(3)：644-649,6.DOI:10.3969/j.issn.0372-2112.2017.03.021
基于softmax回归与图割法的脑肿瘤分割算法
A Brain Tumor Segmentation Method Based on Softmax Regression and Graph Cut
摘要
Abstract
Brain tumor segmentation from medical images is a clinical requirement for brain tumor diagnosis and radiotherapy planning.However,automatic or semi-automatic segmentation of the brain tumor is still a challenging task due to the high diversities and the ambiguous boundaries in the appearance of tumor tissue.To solve this problem,we propose a brain tumor segmentation method based on softmax regression and graph model.Firstly,the training samples are labeled from the multi-modality magnetic resonance images (MRI).Then,the softmax regression method is used to train the samples to obtain the parameters of this regression model and calculate the probabilities of each pixel belonging to different labels.At last,the probabilities calculated in the previous step are introduced to a graph-cut based model.This model is minimized with a min-cut/max-flow method to obtain the final tumor segmentation results.The experiment results demonstrate superior performance in brain tumor segmentation.关键词
医学图像/脑肿瘤/核磁共振图像/图像分割/softmax回归/图割法/最小切/最大流Key words
medical image/brain tumor/magnetic resonance image/image segmentation/softmax regression/graph-cut/min-cut/max-flow分类
信息技术与安全科学引用本文复制引用
葛婷,牟宁,李黎..基于softmax回归与图割法的脑肿瘤分割算法[J].电子学报,2017,45(3):644-649,6.基金项目
国家自然科学基金(No.61275198,No.60978069) （No.61275198,No.60978069）
