Osteoporosis Detection and Classification of Femur X-ray Images Through Spectral Domain Analysis using Texture Features

Osteoporosis commonly diagnosed as a bone disorder that affects the significant portion of the population. The Dual X-ray Absorptiometry (DXA) is one of the most accepted standard methods of analyzing the bone disorder, but it is exorbitant. However X-ray is a cost effective, therefore the proposed...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2023, Vol.14 (9)
Main Authors: A, Dhanyavathi, B, Veena M
Format: Article
Language:English
Subjects:
Bone diseases
Classification
Cross correlation
Discrete Wavelet Transform
Femur
Image enhancement
Mathematical analysis
Osteoporosis
Performance measurement
Texture
Wavelet transforms
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Summary:Osteoporosis commonly diagnosed as a bone disorder that affects the significant portion of the population. The Dual X-ray Absorptiometry (DXA) is one of the most accepted standard methods of analyzing the bone disorder, but it is exorbitant. However X-ray is a cost effective, therefore the proposed work introduces a new technique to improve osteoporosis detection and classification of femur bone X-ray image. The spectral based sub band images texture features are used to analyze the Region Of Interest (ROI) femoral head trabecular bone. A spectral domain based on the Two-Dimensional Discrete Wavelet Transform (2D-DWT) is used to represent variations in finer details in the image. Trabecular femur bone texture is determined only by horizontal, vertical, and diagonal sub bands of DWT coefficients. The sub band images are further enhanced by applying the maximum response filter (MRF) at different scales, thereby enhancing the most significant responses. Consequently, the sum of the MRFs of different scale images is considered as the supervised database. To detect osteoporosis, the test and supervised images are analyzed to calculate two significant attributes such as Zero Mean Normalized Cross-Correlation (ZMNC) and Sum Squared Difference (SSD). Based on experimental results, the performance metrics measure is improved in all aspects over current methods.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.0140980