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Genetic algorithm and image processing for osteoporosis diagnosis

机译:遗传算法和图像处理对骨质疏松症的诊断

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摘要

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.
机译:骨质疏松症被认为是主要的公共卫生威胁。它的特点是骨密度降低,强度降低并导致骨折的风险增加。在这项工作中,使用人工智能和最近开发的骨架化算法评估了2个关节炎和骨质疏松性小梁骨样本种群的形态,拓扑和力学特征。结果表明,与图像处理工具相关的遗传算法可以精确地分离这两个种群。

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