Automatic Liver Segmentation From CT Volumes Based on Level Set and Shape Descriptor

被引：0
|
作者：
Li Y. [1 ,2 ]
Zhao Y.-Q. [1 ,3 ,4 ]
Liao M. [1 ]
Liao S.-H. [1 ]
Yang Z. [5 ]
机构：
[1] School of Automation, Central South University, Changsha
[2] School of Computer Science and Engineering, Central South University, Changsha
[3] Hunan Engineering & Technology Research Center of High Strength Fastener Intelligent Manufacturing, Changde
[4] DeepBlue Technology (Shanghai) Co., Ltd, Shanghai
[5] Xiangya Hospital, Central South University, Changsha
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 02期
基金：
中国博士后科学基金; 中国国家自然科学基金; 湖南省自然科学基金;
关键词：
Abdominal CT image; Level set; Liver segmentation; Shape descriptor;
D O I：
10.16383/j.aas.c180544
中图分类号：
学科分类号：
摘要：
Liver segmentation is an important prerequisite and basis for computer-assisted liver disease diagnosis. This paper proposes a novel method for automatic liver segmentation from CT volume based on level set and shape descriptor. First, irrelevant tissues and organs are removed from original CT volume. Then, intensity bias field together with perimeter term, distance regular term, and segmentation result of neighbor slice is utilized to construct a level set energy function, through which initial liver segmentation results for the CT volume are generated automatically. To avoid segmentation error accumulation, a liver boundary refinement method based on shape descriptor and bottleneck rate is proposed for each initial segmented slice to remove over-segmentation regions caused by intensity overlap. The experiments on CT volumes from XHCSU14 and Sliver07 databases, as well as the comparison with other algorithms show that our method can segment livers effectively with high accuracy and robustness. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：327 / 337
页数：10
相关论文
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