Osteoporosis is an age-based disease causing skeletal disorder. It is described by the decreased bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy patients and osteoporotic cases show a great resemblance. The texture representation is done using two type of methods: appearance based methods and feature based methods. This study explains two systems, one based on PCA and one based on LDA. The system contains two stages, first one is PCA or LDA based feature computation and the second is the classification stage. The classification has been done using classifiers i.e. kNN, NB and SVM. The discriminating power of the texture descriptors is assessed using ten-folded cross-validation scheme using different machine learning techniques. The scope of the study is to support therapists in osteoporosis prediction, avoiding unnecessary further testing with bone.
Hina Ajmal, Saad Rehman, Farhan Riaz, Ali Hassan, Aqib Perwaiz, and Qurrat U. Ain, "PCA and LDA based classifiers for osteoporosis identification," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490K (Presented at SPIE Defense + Security: April 19, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2304728.
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