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

GE Ting; MU Ning; LI Li

doi:10.3969/j.issn.0372-2112.2017.03.021

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

更新时间：2025-07-16

- A Brain Tumor Segmentation Method Based on Softmax Regression and Graph Cut
- Acta Electronica Sinica Vol. 45, Issue 3, Pages: 644-649(2017)
- 作者机构：
  
  1. 金陵科技学院公共基础课部,江苏,南京,211169
  2. 南京理工大学电子工程与光电技术学院,江苏,南京,210094
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61275198, No.60978069)
- DOI：10.3969/j.issn.0372-2112.2017.03.021
  CLC： TP391.41
- Published：2017
- 稿件说明：
移动端阅览
GE Ting, MU Ning, LI Li. A Brain Tumor Segmentation Method Based on Softmax Regression and Graph Cut[J]. Acta Electronica Sinica, 2017, 45(3): 644-649.
DOI：

GE Ting, MU Ning, LI Li. A Brain Tumor Segmentation Method Based on Softmax Regression and Graph Cut[J]. Acta Electronica Sinica, 2017, 45(3): 644-649. DOI： 10.3969/j.issn.0372-2112.2017.03.021.

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.

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.

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