Akademik Veri Yönetim Sistemi
2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Türkiye, 25 - 27 Ağustos 2021
By various hip joint disorders, human proximal femurs are deformed and the natural pure shape of the human proximal femurs is lost. Automatic classification of the proximal femur deformities emerging as a result of the hip diseases is important for the computer-aided diagnosis. In this paper, a study on automatic classification of Waldenstrom stages of Legg-Calve-Perthes disease (LCPD) patients from the 2D total deformity occurring on the proximal femurs is presented. Within the scope of the study, total deformities of the pathological proximal femurs were quantified initially by using a novel and hip joint symmetry based method that we reported in our previous research studies. To this end, total deformities of the pathological proximal femurs were represented as rational values. To classify the Waldenstrom stages of LCPD automatically, the Support Vector Machine (SVM) classifier was employed. Within the experimental studies performed on a dataset consisting of 13 magnetic resonance (MR) images of 13 patients with LCPD, an accuracy about 62% was observed in automatic classification of the Waldenstrom stages of the LCPD patients. The main contribution of the paper is that it is the first study so far, to the best of our knowledge, for the computer vision and machine learning based automatic classification of Waldenstrom stages of LCPD.