Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging
Zehani, Soraya; Ouahabi, Abdeldjalil; Oussalah, Mourad; Mimi, Malika; Taleb-Ahmed, Abdelmalik (2020-10-28)
Zehani, S, Ouahabi, A, Oussalah, M, Mimi, M, Taleb-Ahmed, A. Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging. Int J Imaging Syst Technol. 2021; 31: 141– 159. https://doi.org/10.1002/ima.22512
© 2020 Wiley Periodicals LLC. This is the peer reviewed version of the following article: Zehani, S, Ouahabi, A, Oussalah, M, Mimi, M, Taleb-Ahmed, A. Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging. Int J Imaging Syst Technol. 2021; 31: 141– 159, which has been published in final form at https://doi.org/10.1002/ima.22512. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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https://urn.fi/URN:NBN:fi-fe2023041135745
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Abstract
This paper suggests a new technique for trabecular bone characterization using fractal analysis of X-Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box-counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of \(\frac{N}{8} \log_2 \frac{N}{8}\) where \(N\) stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P-value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X-Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model-based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.
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