Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women.

OBJECTIVE To determine whether individual measurements or a combination of textural features and mandibular cortical width (MCW) derived from digital dental panoramic radiographs (DPRs) are more useful in assessment of osteoporosis. STUDY DESIGN Textural features were obtained by using fractal dimension (FD) and gray-level co-occurrence matrix (GLCM). Digital DPRs and bone mineral densities (BMDs) of the lumbar spine and the femoral neck were obtained from 141 female patients. A naïve Bayes classifier, a k-nearest neighbor (k-NN) algorithm, and a support vector machine were assessed for classifying osteoporosis. RESULTS The combinations of FD plus MCW (95.3%, 92.1%, 96.8%) and GLCM plus MCW (93.7%, 89.5%, 94.2%) for femoral neck BMD showed the highest diagnostic accuracy with the use of the naïve Bayes, k-NN, and support vector machine classifiers, respectively. CONCLUSIONS The combination of textural features and MCW contributed a better assessment of osteoporosis compared with the use of only individual measurements.

[1]  Reinhilde Jacobs,et al.  Diagnosing osteoporosis by using dental panoramic radiographs: the OSTEODENT project. , 2007, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[2]  J. Lee,et al.  Usefulness of estimated height loss for detection of osteoporosis in women. , 2011, Journal of Korean Academy of Nursing.

[3]  S. Brooks,et al.  Oral radiographs in the detection of early signs of osteoporosis. , 1992, Oral surgery, oral medicine, and oral pathology.

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  S C White,et al.  Oral radiographic predictors of osteoporosis. , 2002, Dento maxillo facial radiology.

[6]  R. Schubert,et al.  Texture Analysis, Bone Mineral Density, and Cortical Thickness of the Proximal Femur: Fracture Risk Prediction , 2010, Journal of computer assisted tomography.

[7]  A. Taguchi,et al.  Oral signs as indicators of possible osteoporosis in elderly women. , 1995, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[8]  J. Brickmann.  B. Mandelbrot: The Fractal Geometry of Nature, Freeman and Co., San Francisco 1982. 460 Seiten, Preis: £ 22,75. , 1985 .

[9]  Ilias Maglogiannis,et al.  An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers , 2009, Applied Intelligence.

[10]  H. Imhof,et al.  Imaging of trabecular bone structure in osteoporosis , 1999, European Radiology.

[11]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[12]  A. Taguchi,et al.  Fractal dimension on dental radiographs. , 2001, Dento maxillo facial radiology.

[13]  V. Swarnakar,et al.  Fractal analysis of bone images , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[14]  E. Klemetti,et al.  Morphology of the mandibular cortex on panoramic radiographs as an indicator of bone quality. , 1997, Dento maxillo facial radiology.

[15]  S. Majumdar,et al.  Morphometric texture analysis of spinal trabecular bone structure assessed using orthogonal radiographic projections. , 1998, Medical physics.

[16]  Min-Suk Heo,et al.  Anatomical variations of trabecular bone structure in intraoral radiographs using fractal and particles count analyses , 2012, Imaging science in dentistry.

[17]  Akira Asano,et al.  Computer-aided measurement of mandibular cortical width on dental panoramic radiographs for identifying osteoporosis. , 2012, Journal of investigative and clinical dentistry.

[18]  Rachid Harba,et al.  Changes in Fractal Dimension of Trabecular Bone in Osteoporosis: A Preliminary Study , 1994 .

[19]  Akira Asano,et al.  The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis , 2013, Imaging science in dentistry.

[20]  M. Pithioux,et al.  Combination of texture analysis and bone mineral density improves the prediction of fracture load in human femurs , 2011, Osteoporosis International.

[21]  T. Spector,et al.  The assessment of vertebral deformity: A method for use in population studies and clinical trials , 1993, Osteoporosis International.

[22]  A. Asano,et al.  Computer-aided system for measuring the mandibular cortical width on dental panoramic radiographs in identifying postmenopausal women with low bone mineral density , 2006, Osteoporosis International.

[23]  S C White,et al.  Alterations of the trabecular pattern of the jaws in patients with osteoporosis. , 1999, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[24]  Fahmi Oscandar,et al.  Description of mandibular bone quality based on measurements of cortical thickness using Mental Index of male and female patients between 40-60 years old , 2011, Imaging science in dentistry.

[25]  F. Yaşar,et al.  Evaluating Mandibular Cortical Index Quantitatively , 2008, European journal of dentistry.

[26]  A. Law,et al.  Detecting osteoporosis using dental radiographs: a comparison of four methods. , 1996, Journal of the American Dental Association.

[27]  Hajime Orimo,et al.  Diagnostic criteria for primary osteoporosis: year 2000 revision , 2001, Journal of Bone and Mineral Metabolism.

[28]  C. Cooper,et al.  Osteoporosis in the European Union: medical management, epidemiology and economic burden , 2013, Archives of Osteoporosis.

[29]  Sang Min Park,et al.  Prevalence, awareness, and treatment of osteoporosis among Korean women: The Fourth Korea National Health and Nutrition Examination Survey. , 2012, Bone.

[30]  Harry K. Genant,et al.  Interim Report and Recommendations of the World Health Organization Task-Force for Osteoporosis , 1999, Osteoporosis International.

[31]  Igor Kononenko,et al.  Analysing and improving the diagnosis of ischaemic heart disease with machine learning , 1999, Artif. Intell. Medicine.

[32]  Takio Kurita,et al.  Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system , 2012, BMC Medical Imaging.

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

[34]  F. Yaşar,et al.  The differences in panoramic mandibular indices and fractal dimension between patients with and without spinal osteoporosis. , 2006, Dento maxillo facial radiology.

[35]  Kemal Polat,et al.  A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems , 2009, Expert Syst. Appl..

[36]  Hermann Ney,et al.  Automatic categorization of medical images for content-based retrieval and data mining. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[37]  Hamid Soltanian-Zadeh,et al.  Multiwavelet grading of pathological images of prostate , 2003, IEEE Transactions on Biomedical Engineering.

[38]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[39]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[40]  X Ouyang,et al.  Texture analysis of direct magnification radiographs of vertebral specimens: correlation with bone mineral density and biomechanical properties. , 1997, Academic radiology.

[41]  J. Hokanson,et al.  Diagnostic capabilities of fractal dimension and mandibular cortical width to identify men and women with decreased bone mineral density , 2012, Osteoporosis International.

[42]  T. Southard,et al.  Detection of simulated osteoporosis in maxillae using radiographic texture analysis , 1996, IEEE Transactions on Biomedical Engineering.

[43]  A. Mundinger,et al.  Quantitative image analysis of vertebral body architecture--improved diagnosis in osteoporosis based on high-resolution computed tomography. , 1993, The British journal of radiology.

[44]  Kwang-Joon Koh,et al.  Prediction of age-related osteoporosis using fractal analysis on panoramic radiographs , 2012, Imaging science in dentistry.

[45]  L. Melton,et al.  Adverse Outcomes of Osteoporotic Fractures in the General Population , 2003, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[46]  W. Ohley,et al.  Fractal Analysis of Radiographic Trabecular Bone Texture and Bone Mineral Density: Two Complementary Parameters Related to Osteoporotic Fractures , 2001, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[47]  James Graham,et al.  Image Texture in Dental Panoramic Radiographs as a Potential Biomarker of Osteoporosis , 2013, IEEE Transactions on Biomedical Engineering.

[48]  S. Moon,et al.  Development and validation of osteoporosis risk-assessment model for Korean postmenopausal women , 2013, Journal of Bone and Mineral Metabolism.