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用于骨质疏松症诊断的遗传算法与图像处理

Genetic algorithm and image processing for osteoporosis diagnosis.

作者信息

Jennane R, Almhdie-Imjabber A, Hambli R, Ucan O N, Benhamou C L

机构信息

PRISME institute of the University of Orleans, France.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5597-600. doi: 10.1109/IEMBS.2010.5626804.

Abstract

Osteoporosis is considered as a major public health threat. It is characterized by a decrease in the density of bone, decreasing its strength and leading to an increased risk of fracture. In this work, the morphological, topological and mechanical characteristics of 2 populations of arthritic and osteoporotic trabecular bone samples are evaluated using artificial intelligence and recently developed skeletonization algorithms. Results show that genetic algorithms associated with image processing tools can precisely separate the 2 populations.

摘要

骨质疏松症被视为一项重大的公共卫生威胁。其特征是骨密度降低,骨强度下降,骨折风险增加。在这项研究中,利用人工智能和最近开发的骨骼化算法,对两组关节炎性和骨质疏松性小梁骨样本的形态、拓扑和力学特征进行了评估。结果表明,与图像处理工具相关的遗传算法能够精确区分这两组样本。

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