A Hybrid Model for Liver Shape Segmentation with Customized Fast Marching and Improved GMM-EM

被引：1

作者：

Huang, Weizhuo ^{[1
]}

Zhan, Yinwei ^{[1
]}

Yang, Rongqian ^{[2
]}

机构：

[1] Guangdong Univ Technol, Sch Comp, Guangzhou, Peoples R China

[2] South China Univ Technol, Sch Mat Sci & Engn, Guangzhou, Peoples R China

来源：

IMAGE AND GRAPHICS, ICIG 2019, PT II | 2019年 / 11902卷

关键词：

Liver segmentation; Fast Marching; GMM-EM; K-means plus; LEVEL-SET METHOD; CT IMAGES; BOUNDARY; SPEED;

D O I：

10.1007/978-3-030-34110-7_41

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes an approach to segment liver shape from abdominal CT sequences, required by the analysis of liver diseases. Arough segmentation is first conducted via a customized Fast Marching method to obtain an approximate 3D liver region for subsequent procedure. Then, an improvement of GMM-EM algorithm is made to extract the accurate liver region. Experimental results, evaluated on non-tumor series and tumor series of 10 cases, show that the proposed method performs better than several other typical segmentation models in running time and precision.

引用

页码：495 / 508

页数：14

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