title: Classification of trabecular bone texture from MRI and CT scan images by multi resolution analysis.
abstract: Although bone mineral density measurements constitute one of the main clinical indicators of osteoporosis, we know that bone fragility risk is also related to deteriorations of osseous architecture. Medical imaging constitutes one means to appreciate in vivo bone screen, what is particularly important in the follow up of the osteoporosis. This paper presents a method of bone textural MRI and CT scan classification, based on the use of multifractal analysis by the WTMM-2d method, we propose the choice of three features to realize these images classification: the Hölder exponents average at the peaks of Legendre spectrums, the wavelet transform skeletons density by pixel, and variance of directions of gradients. The preliminary results of 40 images directly resulting from two medical imaging (MRI and CT scan), prove to be interesting since 90% of cases are well estimated, and two classes instantaneous clustering of the results (one healthy patient class and one osteoporotic patient class) quite separate.
authors.affiliation: LAMIH UMR CNRS 8530, Valenciennes and Hainaut Cambresis University, Valenciennes, France.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Khider
authors.forename: Abdelmalik
authors.initials: A
authors.lastname: Taleb-Ahmed
authors.forename: Patrick
authors.initials: P
authors.lastname: Dubois
authors.forename: Boualem
authors.initials: B
authors.lastname: Haddad
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000000981:diagnostic imaging* / Q000473:pathology*; D016000:Cluster Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010024:Osteoporosis / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011859:Radiography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity