Multi-Feature Guided Brain Tumor Segmentation Based on Magnetic Resonance Images

被引：0

作者：

Ai, Ye ^{[1
]}

Miao, Feng ^{[1
]}

Hu, Qingmao ^{[2
]}

Li, Weifeng ^{[1
]}

机构：

[1] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen Key Lab Informat Sci & Technol, Dept Elect Engn, Shenzhen, Peoples R China

[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2015年 / E98D卷 / 12期

基金：

中国国家自然科学基金;

关键词：

tumor segmentation; feature fusion; graph cut; MRI; ENERGY MINIMIZATION; GRAPH CUTS;

D O I：

10.1587/transinf.2015EDP7083

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a novel method of high-grade brain tumor segmentation from multi-sequence magnetic resonance images is presented. Firstly, a Gaussian mixture model (GMM) is introduced to derive an initial posterior probability by fitting the fluid attenuation inversion recovery histogram. Secondly, some grayscale and region properties are extracted from different sequences. Thirdly, grayscale and region characteristics with different weights are proposed to adjust the posterior probability. Finally, a cost function based on the posterior probability and neighborhood information is formulated and optimized via graph cut. Experiment results on a public dataset with 20 high-grade brain tumor patient images show the proposed method could achieve a dice coefficient of 78%, which is higher than the standard graph cut algorithm without a probability-adjusting step or some other cost function-based methods.

引用

页码：2250 / 2256

页数：7

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