Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population

SummaryWe propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects.IntroductionWe propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers.MethodsCortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass.ResultsIn our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%.ConclusionAn automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

[1]  H. H. Thodberg,et al.  A paediatric bone index derived by automated radiogrammetry , 2009, Osteoporosis International.

[2]  H. H. Thodberg,et al.  Estimation of Bone Mineral Density by Digital X-ray Radiogrammetry: Theoretical Background and Clinical Testing , 2001, Osteoporosis International.

[3]  S. Cummings,et al.  Standardising the descriptive epidemiology of osteoporosis: recommendations from the Epidemiology and Quality of Life Working Group of IOF , 2013, Osteoporosis International.

[4]  Ji-Wook Jeong,et al.  A preliminary study on discrimination of osteoporotic fractured group from nonfractured group using support vector machine , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[6]  A. S. Areeckal,et al.  Fully automated radiogrammetric measurement of third metacarpal bone from hand radiograph , 2016, 2016 International Conference on Signal Processing and Communications (SPCOM).

[7]  B E NORDIN,et al.  The radiological diagnosis of osteoporosis: a new approach. , 1960, Clinical radiology.

[8]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

[9]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[10]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .