Comparative study of local binary pattern and its shifted variant for osteoporosis identification

Osteoporosis is an age-based disease causing skeletal disorder. It is described by the little bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy subjects and osteoporotic cases show a high grade of resemblance. This study presents an evaluation of osteoporosis identification using texture descriptor Local Binary Pattern (LBP) and Shift Local Binary Pattern (SLBP). In contrast with the conventional LBP, with the shifted LBP specific number of binary local codes are generated for each pixel place. The distinguishing ability of the texture descriptors is evaluated using ten-fold cross validation and leave-one out scheme using different machine learning techniques. The results prove the SLBP outperforms the traditional LBP for bone texture characterization.

[1]  Abdelmalik Taleb-Ahmed,et al.  Analysis methods of CT-scan images for the characterization of the bone texture: First results , 2003, Pattern Recognit. Lett..

[2]  J. Pramudito,et al.  Trabecular Pattern Analysis of Proximal Femur Radiographs for Osteoporosis Detection , 2007 .

[3]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[4]  A. Hofman,et al.  Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. , 2004, Bone.

[5]  Ida-Maria Sintorn,et al.  Evaluation of noise robustness for local binary pattern descriptors in texture classification , 2013, EURASIP J. Image Video Process..

[6]  D Chappard,et al.  Trabecular microarchitecture in established osteoporosis: relationship between vertebrae, distal radius and calcaneus by X-ray imaging texture analysis. , 2013, Orthopaedics & traumatology, surgery & research : OTSR.

[7]  JoAnn E. Manson,et al.  Effects of estrogen plus progestin on health-related quality of life. , 2003, The New England journal of medicine.

[8]  Rachid Harba,et al.  Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection , 2007, Medical Image Anal..

[9]  C. L. Benhamou,et al.  Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study , 2008, Osteoporosis International.

[10]  S. Kay,et al.  Fractional Brownian Motion: A Maximum Likelihood Estimator and Its Application to Image Texture , 1986, IEEE Transactions on Medical Imaging.

[11]  S. Giannini,et al.  Bone microarchitecture as an important determinant of bone strength , 2004, Journal of endocrinological investigation.

[12]  S. Cummings,et al.  Bone mass measurements and risk of fracture in Caucasian women: a review of findings from prospective studies. , 1995, The American journal of medicine.

[13]  Roberto Rossi,et al.  Medical imaging and osteoporosis: fractal's lacunarity analysis of trabecular bone in MR images , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[14]  Aouatif Amine,et al.  Trabecular bone characterization using circular parametric models , 2017, Biomed. Signal Process. Control..

[15]  C. Benhamou,et al.  Fractal Analysis of Trabecular Bone Texture on Radiographs: Discriminant Value in Postmenopausal Osteoporosis , 1998, Osteoporosis International.

[16]  Rachid Jennane,et al.  Biomedical Signal Processing and Control , 2013 .

[17]  Adel Hafiane,et al.  One dimensional local binary pattern for bone texture characterization , 2012, Pattern Analysis and Applications.