多重分形分析采用大偏差谱检测骨质疏松症

M. Khider, B. Haddad, Abdelmalik Taleb Ahmed
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引用次数: 2

摘要

本工作是利用大偏差理论计算颗粒多重分形谱并对骨微结构纹理进行分类,为此采用多重分形谱模式,给出了优势分形集的分形维数来检测骨质疏松症。事实上,在ROI小梁纹理中,区分病理和正常情况最相关的参数之一就是骨微结构中小梁之间的分离距离。本文提出的方法是基于多重分形分析对骨小梁厚度序列和骨小梁分离序列所形成的信号进行分形分析,并从骨小梁纹理的灰度强度中获得信号,对两种研究案例进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multifractal analysis by the large deviation spectrum to detect osteoporosis
This work is based on the use of the theory of large deviations to calculate the grain multifractal spectrum and classify bone micro architecture texture, to do this the multifractal spectrum mode is used, it gives the fractal dimension of the predominant fractal set to detect osteoporosis. In fact, one of the most relevant parameters to differentiate between pathological and normal cases in the trabecular ROI texture is the distance of separation between trabeculae in bone micro architecture. The method we propose here is based on the multifractal analysis of the signal formed by the succession of bone trabecular thickness and trabecular separation obtained from gray level intensities in the trabecular bone texture to classify the two cases of study.
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