电子学报 ›› 2017, Vol. 45 ›› Issue (3): 644-649.DOI: 10.3969/j.issn.0372-2112.2017.03.021

所属专题: 机器学习与智慧医疗

• 学术论文 • 上一篇    下一篇

基于softmax回归与图割法的脑肿瘤分割算法

葛婷1,2, 牟宁1, 李黎1   

  1. 1. 南京理工大学电子工程与光电技术学院, 江苏南京 210094;
    2. 金陵科技学院公共基础课部, 江苏南京 211169
  • 收稿日期:2015-09-25 修回日期:2016-02-19 出版日期:2017-03-25 发布日期:2017-03-25
  • 作者简介:葛婷 女,1981年生于江苏南京.现为南京理工大学电子工程与光电技术学院博士研究生,金陵科技学院讲师.主要研究方向为医学图像处理与分析.E-mail:geting@jit.edu.cn;牟宁 女,1981年生于江苏南京.现为南京理工大学电子工程与光电技术学院博士研究生.主要研究方向为生物医学成像.E-mail:ning.mu@gmail.com;李黎 女,1981年生于安徽蚌埠,现为南京理工大学电子工程与光电技术学院博士研究生.河海大学讲师.主要研究方向为毫米波天线系统.E-mail:lili@hhu.edu.cn
  • 基金资助:

    国家自然科学基金(No.61275198,No.60978069)

A Brain Tumor Segmentation Method Based on Softmax Regression and Graph Cut

GE Ting1,2, MU Ning1, LI Li1   

  1. 1. School of Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China;
    2. Department of Mathematical, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China
  • Received:2015-09-25 Revised:2016-02-19 Online:2017-03-25 Published:2017-03-25

摘要:

从医学图像中分割脑肿瘤区域可以为脑肿瘤的诊断以及放射治疗提供帮助.但肿瘤区域的变化异常且边界非常模糊,因此自动或半自动地分割脑肿瘤非常困难.针对这一问题,本文结合softmax回归和图割法提出一种脑肿瘤分割算法.首先融合多序列核磁共振图像(MRI)并标记训练样本,再用softmax回归训练模型参数并计算每个点属于各个类别的概率,最后将概率融入到图割法中,用最小切/最大流方法得到最终分割结果.实验表明提出的方法可以更好地得到脑肿瘤的边界,并能较准确地分割出脑肿瘤区域.

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

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.

Key words: medical image, brain tumor, magnetic resonance image, image segmentation, softmax regression, graph-cut, min-cut/max-flow

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