Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 36; no. 6; p. 945
• Main Authors: Xiong, Xiaoliang, Guo, Yi, Wang, Yuanyuan, Zhang, Dai, Ye, Zhaoxiang, Zhang, Sheng, Xin, Xiaojie
• Format: Journal Article
• Language: Chinese
• Published: China Sichuan Society for Biomedical Engineering 25.12.2019
• Subjects: Algorithms; Centroids; Convolution; Evolution; Fetal Growth Retardation; Health risks; Humans; Image contrast; Image processing; Image Processing, Computer-Assisted; Image segmentation; Kidney Neoplasms; Kidneys; Medical imaging; Metric space; Organs; Osteochondrodysplasias; Radiation; Tumors; Ultrasonic imaging; Ultrasonic methods; Ultrasonic testing; Ultrasonography; Ultrasound; kidney tumor; ultrasound image; centroid; level set; adaptive sub-regional evolution
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.201902011

Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) o