Classification of long-bone fractures based on digital-geometric analysis of X-ray images

The classification of fractured of a patient plays an important role in orthopaedic evaluation and diagnosis. It not only aids in assessing the severity of the disease or injury but also serves as a basis of treatment or surgical correction. This paper proposes a novel approach to automated classification of long-bone fractures based on the analysis of an input X-ray image. The method consists of four major steps: (i) extraction of the bone-contour from a given X-ray image, (ii) identification of fracture-points or cracks, (iii) determination of an equivalent set of geometric features in tune with the Müller-AO clinical classification of fractures, and (iv) identification and detailed assessment of the fracture-type. The decision procedure makes use of certain geometric properties of digital curves such as relaxed digital straight line segments (RDSS), arcs, discrete curvature, and concavity index. The proposed method for the analysis of fractures is applied on different types of bone-images and is observed to have produced correct classification in most of the test-cases.

[1]  Fan Hongqi,et al.  Feature Extraction of X-ray Fracture Image and Fracture Classification , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[2]  Arindam Biswas,et al.  Long-bone fracture detection in digital X-ray images based on digital-geometric techniques , 2016, Comput. Methods Programs Biomed..

[3]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[4]  P. D. M. E. Müller,et al.  The Comprehensive Classification of Fractures of Long Bones , 1990, Springer Berlin Heidelberg.

[5]  G. Gard,et al.  Identifying work ability promoting factors for home care aides and assistant nurses , 2012, BMC Musculoskeletal Disorders.

[6]  Bao-Chang Pan,et al.  Image segmentation of bone in X-ray pictures of feet , 2009, 2009 International Conference on Wavelet Analysis and Pattern Recognition.

[7]  Isabelle Bloch,et al.  A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images , 2016, Medical Image Anal..

[8]  Martin Donnelley,et al.  A CAD System for Long-Bone Segmentation and Fracture Detection , 2008, ICISP.

[9]  Gábor Székely,et al.  Computer assisted reconstruction of complex proximal humerus fractures for preoperative planning , 2012, Medical Image Anal..

[10]  F.,et al.  GARDEN’S CLASSIFICATION OF FEMORAL NECK FRACFURES , 2005 .

[11]  R. West,et al.  New classification system for long-bone fractures supplementing the AO/OTA classification. , 2012, Orthopedics.

[12]  S. Leopold,et al.  Classifications in Brief: The Neer Classification for Proximal Humerus Fractures , 2013, Clinical orthopaedics and related research.

[13]  Sankar K. Pal,et al.  Gradient histogram: Thresholding in a region of interest for edge detection , 2010, Image Vis. Comput..

[14]  Jeongsam Yang,et al.  Development of a decision making system for selection of dental implant abutments based on the fuzzy cognitive map , 2012, Expert Syst. Appl..

[15]  A. Gaidel,et al.  Application of texture analysis for automated osteoporosis diagnostics by plain hip radiography , 2015, Pattern Recognition and Image Analysis.

[16]  Bhabatosh Chanda,et al.  Automatic Segmentation of Bones in X-ray Images Based on Entropy Measure , 2016, Int. J. Image Graph..

[17]  Partha Bhowmick,et al.  Construction of isothetic covers of a digital object: A combinatorial approach , 2010, J. Vis. Commun. Image Represent..

[18]  Bhabatosh Chanda,et al.  Bone Contour Tracing in Digital X-ray Images Based on Adaptive Thresholding , 2013, PReMI.

[19]  E. Sivertsen,et al.  Classification of distal radius fractures in children: good inter- and intraobserver reliability, which improves with clinical experience , 2012, BMC Musculoskeletal Disorders.

[20]  Ying Chen,et al.  Detecting femur fractures by texture analysis of trabeculae , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[21]  C. Stolojescu-Crisan,et al.  A Comparison of X-Ray Image Segmentation Techniques , 2013 .

[22]  Xubo Song,et al.  Model-based image processing and analysis for fracture classification , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[23]  F. Madsen,et al.  Garden's classification of femoral neck fractures. An assessment of inter-observer variation. , 1988, The Journal of bone and joint surgery. British volume.

[24]  A. Srinivasan,et al.  Applications of deformable models for in-dopth analysis and feature extraction from medical images—A review , 2013, Pattern Recognition and Image Analysis.

[25]  A. Mehrabi,et al.  A new way for surgical education - development and evaluation of a computer-based training module , 2000, Comput. Biol. Medicine.

[26]  Ying Chen,et al.  Computing Neck-Shaft Angle of Femur for X-Ray Fracture Detection , 2003, CAIP.

[27]  Partha Bhowmick,et al.  Estimation of discrete curvature based on chain-code pairing and digital straightness , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[28]  Chee Peng Lim,et al.  A hybrid intelligent system for medical data classification , 2014, Expert Syst. Appl..

[29]  Parameswaran Ramanathan,et al.  A Virtual Reality Based Simulation Environment for Orthopedic Surgery , 2014, OTM Workshops.

[30]  Partha Bhowmick,et al.  Circular Arc Segmentation by Curvature estimation and Geometric Validation , 2012, Int. J. Image Graph..

[31]  Arindam Biswas,et al.  Long-Bone Fracture Detection in Digital X-ray Images Based on Concavity Index , 2014, IWCIA.

[32]  Tan Tian Swee,et al.  Gray-Level Co-occurrence Matrix Bone Fracture Detection , 2011 .

[33]  Juan José Jiménez-Delgado,et al.  Computer assisted preoperative planning of bone fracture reduction: Simulation techniques and new trends , 2016, Medical Image Anal..

[34]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .