Improved graph-cut segmentation for ultrasound liver cyst image

被引：3
|
作者：
Zhu, Haijiang [1 ]
Zhuang, Zhanhong [1 ]
Zhou, Jinglin [1 ]
Wang, Xuejing [2 ]
Xu, Wenhua [3 ]
机构：
[1] Beijing Univ Chem Technol, Coll Informat & Technol, Beijing 100029, Peoples R China
[2] Beijing Univ Chem Technol, Informat Ctr, Beijing 100029, Peoples R China
[3] Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou 510006, Guangdong, Peoples R China
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2018年 / 77卷 / 21期
基金：
中国国家自然科学基金;
关键词：
Ultrasound liver cyst segmentation; Graph-based method; PSO; NEURAL-NETWORK; LOCATION;
D O I：
10.1007/s11042-018-6076-4
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An optimal contour segmentation for ultrasonic liver cyst image is presented through combining graph-based method with particle swarm optimization (PSO) in this paper. After automatic selecting the region of interest (ROI) for ultrasonic liver cyst image, our method developed firstly a kind of multiple classes merging scheme by jointing the graph-based segmented result with the intensity of original ultrasound image. Then the evaluation function in the PSO was modified to optimize the parameter. Finally, the liver cysts were segmented according to the optimized parameter. In the experiment, we tested the influence of weight value on the improved method. And five indicators, which included Hausdorff distance (HD), mean absolute distance (MD), true positive volume fraction (TPVF), false-negative volume fraction (FNVF) and false-positive volume fraction (FPVF), were estimated to verify the improved method. Experimental results have validated that the improved method may extract successfully and accurately the contour of liver cyst.
引用
收藏
页码：28905 / 28923
页数：19
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