A New Complex Wavelet Relative Phase for Osteoporosis Diagnosis

Trabecular bone (TB) characterization for osteoporosis diagnosis on plain radiographic images presents a challenging task in medical imaging. The goal of this paper is to study the information of complex wavelet coefficients to extract descriptors of the trabecular bone. These descriptors are extracted using some statistics of a new relative phase coefficients. The relative phase modeling is performed using two well-known circular models, called Von Mises and Wrapped Cauchy. Both models have the advantages of simple feature extraction using maximum likelihood estimators and similarity measurement of the Kullback-Leibler divergence, which is very helpful in the runtime task. Our proposed approach is used to recognize the osteoporosis patients from a population composed of both osteoporosis patients and control cases. Experimental results on a TB radiograph database show that considering the proposed approach improves the classification performances over the state-of-the-art methods.