<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10008509" mdate="2018-01-29 00:00:00"> <author>I. Slim and H. Akkari and A. Ben Abdallah and I. Bhouri and M. Hedi Bedoui</author> <title>Trabecular Bone Radiograph Characterization Using Fractal, Multifractal Analysis and SVM Classifier</title> <pages>1 - 4</pages> <year>2018</year> <volume>12</volume> <number>1</number> <journal>International Journal of Biomedical and Biological Engineering</journal> <ee>https://publications.waset.org/pdf/10008509</ee> <url>https://publications.waset.org/vol/133</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Osteoporosis is a common disease characterized by low bone mass and deterioration of microarchitectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the qstucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22.</abstract> <index>Open Science Index 133, 2018</index> </article>