Incorporating priors for medical image segmentation using a genetic algorithm

被引：39
作者：
Ghosh, Payel [1 ,2 ]
Mitchell, Melanie [2 ,3 ]
Tanyi, James A. [4 ]
Hung, Arthur Y. [5 ]
机构：
[1] Univ Texas MD Anderson Canc Ctr, Dept Diagnost Radiol, Houston, TX 77030 USA
[2] Portland State Univ, Dept Comp Sci, Portland, OR 97207 USA
[3] Santa Fe Inst, Santa Fe, NM 87501 USA
[4] Oregon Hlth & Sci Univ, Dept Radiat Med, SOM, Portland, OR 97201 USA
[5] Oregon Hlth & Sci Univ, Dept Radiat Med, Portland, OR 97201 USA
来源：
NEUROCOMPUTING | 2016年 / 195卷
关键词：
Medical image segmentation; Genetic Algorithms; Texture Analysis; Shape Modeling; Relative position priors; TEXTURE; POSITION;
D O I：
10.1016/j.neucom.2015.09.123
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation on pelvic computed tomography and magnetic resonance images. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：181 / 194
页数：14
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