Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images
Machine Learning Research
Volume 3, Issue 3, September 2018, Pages: 49-59
Received: Aug. 19, 2018;
Accepted: Sep. 6, 2018;
Published: Oct. 10, 2018
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Wint Wah Myint, Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar
Khin Sandar Tun, Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar
Hla Myo Tun, Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar
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Nowadays, computer aided diagnosis (CAD) system become popular because it improves the interpretation of the medical images compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fracture is a common problem due to pressure, accident and osteoporosis. Moreover, bone is rigid portion and supports the whole body. Therefore, the bone fracture is taken account of the important problem in recent year. Bone fracture detection using computer vision is getting more and more important in CAD system because it can help to reduce workload of the doctor by screening out the easy case. In this paper, lower leg bone (Tibia) fracture types recognition is developed using various image processing techniques. The purpose of this work is to detect fracture or non-fracture and classify type of fracture of the lower leg bone (tibia) in x-ray image. The tibia bone fracture detection system is developed with three main steps. They are preprocessing, feature extraction and classification to classify types of fracture and locate fracture locations. In preprocessing, Unshrap Masking (USM), which is the sharpening technique, is applied to enhance the image and highlight the edges in the image. The sharpened image is then processed by Harris corner detection algorithm to extract corner feature points for feature extraction. And then, two classification approaches are chosen to detect fracture or non-fracture and classify fracture types. For fracture or not classification, simple Decision Tree (DT) is employed and K-Nearest Neighbour (KNN) is used for classifying fracture types. In this work, Normal, Transverse, Oblique and Comminute are defined as the four fracture types. Moreover, fracture locations are pointed out by the produced Harris corner points. Finally, the outputs of the system are evaluated by two performance assessment methods. The first one is performance evaluation for fracture or non-fracture (normal) conditions using four possible outcomes such as TP, TN, FP and FN. The second one is to analysis for accuracy of each fracture type within error conditions using the Kappa assessment method. The programming software used to implement the system is MATLAB with wide range of image processing tools environment. The system produces 82% accuracy for classification fracture types.
Leg Bone Fracture Detection, Classification, X-ray Images, MATLAB, Biomedical Engineering, Machine Learning
To cite this article
Wint Wah Myint,
Khin Sandar Tun,
Hla Myo Tun,
Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images, Machine Learning Research.
Vol. 3, No. 3,
2018, pp. 49-59.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
S. Myint, A. S. Khaing and H. M. Tun, “Detecting Leg Bone Fracture in X-ray Images”, International Journal of Scientific & Research, vol. 5, Jun. 2016, pp. 140-144.
V. D. Vegi and S. L. Patibandla, S. SKavikondala and CMAK Z. Basha, “Computerized Fracture Detection System using x-ray Images”, International Journal of Control Theory and Applications, vol. 9, Nov. 2016, pp. 615-621.
S. K. Mahendran and S. Santhosh Baboo, “An Enhanced Tibia Fracture Detection Tool Using Image Processing and Classification Fusion Techniques in X-Ray Images”, Global Journal Of Computer Science and Technology, vol. 11, Aug. 2011, pp. 27-28.
S. K. Mahendran and S. Santhosh Baboo, “Ensemble Systems for Automatic Fracture Detection”, International Journal of Engineering and Technology (JACSIT), vol. 4, Feb. 2012, pp.7-10.
M. AL-AYYOUB and D. AL-ZGHOOL, “Determining the Type of Long Bone Fracture in X-ray Images”,WSEAS TRANSACATIONS on INFORMATION SCIENCE and APPLICATIONS, vol. 10, Aug. 2013, pp.261-270.
A. T. C, Mallikarjunaswamy M. S. and Rajesh Raman , “Detection of Bone Fracture using Image Processing Methods”, International Journal of Computer Application, National Conference on Power & Industrial Automation (NCPSIA, Aug. 2015, pp. 6-9.
S Jayaraman, S Esakkirajan and T Veerakumar, Digital Image Processing, 2009, pp. 243-274.
CHRIS SOLOMON, TOBY BRECKON, FUNDAMENTALS OF DIGITAL IMAGE PROCESSING,2011 pp. 90-108.
N. Umadevi, Dr. S. N. Geethalakshmi, “Multiple Classification System for Fracture Detection in Human Bone X-ray Images”, in Proc. Third International Conference on Computing Communication & Networking Technologies (ICCCNT), India , 2012, pp. 1-8.
I. Hmneidi and M. Al-Ayyoub, “Detecting Hand Bone Fractures in X-Ray Images”, in Proc. The International Conference on Signal Processing and Imaging, Tunisia, 2013, pp. 10-14.