American Journal of Applied Sciences

Gray-Level Co-occurrence Matrix Bone Fracture Detection

Hum Yan Chai, Lai Khin Wee, Tan Tian Swee, Sh-Hussain Salleh, A.K. Ariff and Kamarulafizam

DOI : 10.3844/ajassp.2011.26.32

American Journal of Applied Sciences

Volume 8, Issue 1

Pages 26-32


Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically.


© 2011 Hum Yan Chai, Lai Khin Wee, Tan Tian Swee, Sh-Hussain Salleh, A.K. Ariff and Kamarulafizam . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.