3D Image Analysis and Artificial Intelligence for Bone Disease Classification

被引:0
作者
Abdurrahim Akgundogdu
Rachid Jennane
Gabriel Aufort
Claude Laurent Benhamou
Osman Nuri Ucan
机构
[1] Istanbul University,Department of Electrical and Electronics Eng
[2] University of Orleans,Instiut PRISME / LESI
[3] Equipe INSERM U658,undefined
[4] Hospital of Orleans,undefined
来源
Journal of Medical Systems | 2010年 / 34卷
关键词
Trabecular bone; Hybrid Skeleton Graph Analysis; SVM; GA; ANFIS;
D O I
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中图分类号
学科分类号
摘要
In order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm.
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页码:815 / 828
页数:13
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