@article{10.11648/j.mlr.20180303.11,
  author = {Wint Wah Myint and Khin Sandar Tun and Hla Myo Tun},
  title = {Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images},
  journal = {Machine Learning Research},
  volume = {3},
  number = {3},
  pages = {49-59},
  doi = {10.11648/j.mlr.20180303.11},
  url = {https://doi.org/10.11648/j.mlr.20180303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20180303.11},
  abstract = {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.},
 year = {2018}
}

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TY  - JOUR
T1  - Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images
AU  - Wint Wah Myint
AU  - Khin Sandar Tun
AU  - Hla Myo Tun
Y1  - 2018/10/10
PY  - 2018
N1  - https://doi.org/10.11648/j.mlr.20180303.11
DO  - 10.11648/j.mlr.20180303.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 49
EP  - 59
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20180303.11
AB  - 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.
VL  - 3
IS  - 3
ER  - 

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