Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the med- ical image, thus causing blurring or uneven characteristics when forming a picture. Such qu...

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Bibliographic Details
Published in:Science China. Information sciences 2014-10, Vol.57 (10), p.154-165
Main Authors: Zhuo, ZiHan, Zhai, WeiMing, Li, Xin, Liu, LingLing, Tang, JinTian
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Blurring
Computer Science
Image contrast
Image segmentation
Information Systems and Communication Service
Leakage
Medical imaging
Research Paper
Robustness
Shape
Statistics
Topology
三维医学图像
分割算法
医学图像分割
图像对比度
统计局
自适应
计算机辅助
输入参数
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Summary:Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the med- ical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics （LARFS） was proposed. By combining segmentation algorithm principles for traditional region growing （RG） and robust feature statistics （RFS）, the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And be- cause the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-014-5095-7