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Trabecular Bone Texture Characterization Using Regularization Dimension and Box-counting Dimension

This paper presents texture characterization techniques for effective diagnosis of osteoporosis cases on bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases with bone radiograph images presents a major challenge as the images show little or no visual difference for both cases. The proposed work uses multifractal analysis to characterize the texture of bone radiographs. A regularization dimension method is newly introduced in this work for texture analysis of bone radiographs. Series of local fractal dimensions are used here instead of single global fractal dimension to characterize local texture variations effectively. The performance of the regularization dimension method is compared to the performance of the box-counting method. Then, regularization dimension and box-counting dimension features are combined to improve the performance. The proposed method is evaluated using texture characterization of bone radiograph (TCB) challenge dataset and compared against other methods participated in the challenge to demonstrate the effectiveness of the proposed method