期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130449
语种：English
出版社：Science and Information Society (SAI)
摘要：One of the most common bone diseases in humans is osteoporosis, which is a major concern for the public health. Osteoporosis can be prevented if it is detected at an early stage. The research agenda consists of two phases: pre-processing of X-ray images of the spine and analysis of texture features from trabecular bone lumbar vertebrae L1-L4 for detecting osteoporosis. The preprocessing involves image enhancement of texture features and co-register the images in order to segment the L1-L4 regions in the lumbar spine. Range filtering and Pyramid Histogram of Orientation Gradient (PHOG) are used to analyze texture features. Input images are filtered with a range filter to adjust the local sub range intensities in a specified window to detect edges. Then a PHOG algorithm is designed to determine both the local shape of an image texture and its spatial layout. Based on texture features of lumbar vertebrae L1-L4, classify them as normal or osteoporotic using neural network (NN) models with L2 regularization. In an experiment, X-ray images and dual-energy X-ray absorptiometry (DXA) reports of individual patients are used to verify the system. DXA reports describe a statistical analysis of normal and osteoporotic results. However, the proposed work is categorized according to the texture features as normal or osteoporotic. 99.34% classification accuracy is achieved; cross-validation of these classified results is done with the DXA reports. Diagnostic accuracy of the proposed method is higher than that of the existing DXA with X-ray. Further, the area under the Receiver Operating Characteristic (ROC) curve for L1-L4 had a significantly higher sensitivity for osteoporosis.
