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Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging
Author(s) -
Zehani Soraya,
Ouahabi Abdeldjalil,
Oussalah Mourad,
Mimi Malika,
TalebAhmed Abdelmalik
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22512
Subject(s) - fractal analysis , osteoporosis , discrete cosine transform , wilcoxon signed rank test , fractal , lacunarity , fractal dimension , computer science , pattern recognition (psychology) , frequency domain , artificial intelligence , mathematics , preprocessor , image (mathematics) , algorithm , medicine , computer vision , statistics , pathology , mathematical analysis , mann–whitney u test
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 N 8 log 2 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.