Osteoporosis vizualization by densities projection based on a kernel convolution method

Walid Ayadi; Sylvie Sevestre-Ghalila; Amel Benazza-Benyahia

doi:10.1117/12.692743

19 October 2006 Osteoporosis vizualization by densities projection based on a kernel convolution method

Walid Ayadi, Sylvie Sevestre-Ghalila, Amel Benazza-Benyahia

Proceedings Volume 6383, Wavelet Applications in Industrial Processing IV; 63830G (2006) https://doi.org/10.1117/12.692743
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

Abstract

Both reduction of the bone mass and a degradation of the microarchitecture of the bone tissue are indicators of the osteoporosis disease. This is why radiographies of the calcaneus are very often used in order to analyze and describe both the texture and the structure of the bone. Therefore, a great effort is devoted to texture analysis by sophisticated image processing tools. In this paper, we propose a method for extracting information from a multiresolution representation of the radiological images that facilitates the graphic detection of the osteoporosis. The main contribution of this work relies on the statistical processing of the wavelet-based extracted features that are employed to graphically discriminate between stwo kinds of Osteoporotic Patients (OP1: vertebral fracture, OP2: other fractures) and Control Patients (CP). Graphical discrimination is obtained by an estimation of patients classes' densities by a multivariate kernel density estimation method, the axes result from a linear discriminant analysis between OP1/CP and OP2/CP. Classification and statistical tests carried out on a set of radiographies with their own ground truth validate the ability of discrimination of the proposed features extracted from M-band wavelet transform

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Walid Ayadi, Sylvie Sevestre-Ghalila, and Amel Benazza-Benyahia "Osteoporosis vizualization by densities projection based on a kernel convolution method", Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830G (19 October 2006); https://doi.org/10.1117/12.692743

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KEYWORDS

Bone

Wavelet transforms

Wavelets

Radiography

Statistical analysis

Feature extraction

Prototyping

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