title: 3D image analysis and artificial intelligence for bone disease classification.
abstract: 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.
authors: Department of Electrical and Electronics Eng, Istanbul University, 34850 Avcilar, Istanbul, Turkey. akgundog@istanbul.edu.tr
authors: Abdurrahim
authors: A
authors: Akgundogdu
authors: Rachid
authors: R
authors: Jennane
authors: Gabriel
authors: G
authors: Aufort
authors: Claude Laurent
authors: CL
authors: Benhamou
authors: Osman Nuri
authors: ON
authors: Ucan
mesh_terms: D001185:Artificial Intelligence; D001842:Bone and Bones / Q000648:ultrastructure*; D020342:Finite Element Analysis; D005598:Fractures, Spontaneous / Q000517:prevention & control; D006801:Humans; D021621:Imaging, Three-Dimensional; D008954:Models, Biological; D010003:Osteoarthritis / Q000145:classification / Q000473:pathology*; D010024:Osteoporosis / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated