Automatic Liver Lesion Extraction from CT Images Based on Distance Regularized Level Set Evolution and Region Growing

被引：2
|
作者：
Gao, Fenglian [1 ]
Huang, Lianfen [1 ]
Wang, Jiakun [1 ]
Shuai, Haitao [2 ]
Sun, Jianjun [2 ]
Huang, Yue [1 ]
机构：
[1] Xiamen Univ, Sch Informat Sci & Technol, Xiamen, Fujian, Peoples R China
[2] PLA, Dept Radiol, Clin Sect Fuzhou Gen Hosp 476, Fuzhou, Fujian, Peoples R China
来源：
MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY | 2014年 / 556-562卷
关键词：
automatic lesion extraction; region growing; liver segmentation; distance regularized level set evolution; ACTIVE CONTOURS; SEGMENTATION;
D O I：
10.4028/www.scientific.net/AMM.556-562.4924
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Current diagnosis with computed tomography (CT) imaging relies heavily on doctors' clinical experience and it is difficult to accurately identify and localize lesions from thousands of CT images. Therefore, computer-aided diagnosis with automatic lesion extraction will be helpful for doctors in the diagnosis of liver diseases. In this paper, we proposed a new method for automatic liver lesion extraction from CT images by combining DRLSE (distance regularized level set evolution) and region growing. The method was applied in abdominal CT images with a single liver cancerous lesion and multiple hemangioma lesions at different locations. The results demonstrated the feasibility of our method for automatic lesion extraction with improved diagnostic accuracy and time efficiency.
引用
收藏
页码：4924 / +
页数：2
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