Abstract
In recent days, computer-aided fracture detection system plays a role in aiding both orthopaedician and a radiologist by providing accurate and fast results. In order to detect the fracture automatically, classification of X-ray images should be automated and it becomes the initial step. Therefore, an attempt has been made and a system is presented in this paper, which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network. To classify the given input X-ray images into the categories head, neck, skull, foot, palm, and spine, the probabilistic neural network, backpropagation neural network, and support vector machine classifiers are employed in classifying X-ray images. The results ascertain an overall accuracy of 92.3% in classifying X-ray images and the presented system can be used as an effective tool for X-ray image classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mahmoud Al-ayoub, Ismail Hmeidi, Haya Rababah, “Detecting hand bone fractures in X-ray images”, Journal of multimedia processing and technologies, Vol. 4, No. 3, pp. 155–168, September 2013.
S. Sai Niveaditha, V. Pavithra, R. Jayashree, T. Tamilselvi, “Online diagnosis of X-ray image using FLDA image processing algorithm”. IRF International Conference, March 2014, pp. 76–80.
Jaskirat Kaur, Sunil Agrawal, Renu Wig, “A comparative analysis of thresholding and edge detection segmentation techniques.” International journal of computer applications. Vol. 39, No. 15, pp. 29–34, 2012.
S.K. Mahendran, “A comparative study on edge detection algorithms for computer aided fracture detection systems”. International journal of engineering and innovative technology. Vol. 2, No. 5, pp. 191–193, November 2012.
Subodh Kumar, Prabat Pandey, “Implementation of X-ray image segmentation by using edge detection using sobel edge operator”, International journal of innovative research and studies, Vol. 3, No. 2, pp. 191–202, February 2014.
Rafeal. C. Gonzalez & Woods, “Chapter 10: Image segmentation”, Digital image processing, Pearson Education, Inc., 2005, pp. 392–439.
Balaji, G. N., T. S. Subashini, and N. Chidambaram. “Automatic Classification of Cardiac Views in Echocardiogram Using Histogram and Statistical Features.” Procedia Computer Science 46 (2015): 1569–1576.
H. Arimura, S. Katsuragawa, Q. Li, T. Isguda, and K. Doi, “Development of a computerized method for identifying the posteroanterior and lateral views of chest radiographs by use of a template matching technique,” Med. Phys., vol. 29, no. 7, pp. 308–315, 2002.
Balaji, G. N., T. S. Subashini, and N. Chidambaram. “Cardiac View Classification Using Speed Up Robust Features.” Indian Journal of Science and Technology 8.S7 (2015): 1–5.
Kim, Seong-Hoon, et al. “X-ray image classification using random forests with local binary patterns.” Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. Vol. 6. IEEE, 2010.
M. Bugdol, J. Czajkowska, and E. Pietka, “A novel model-based approach to left ventricle segmentation,” in Computing in Cardiology (CinC). Krakow, Poland: IEEE, 2012, pp. 561–564.
Ghofrani, Fatemeh, et al. “Fuzzy-Based Medical X-ray Image Classification.” Journal of medical signals and sensors 2.2 (2012): 73.
Mohammadi, Seyyed Mohammad, Mohammad Sadegh Helfroush, and Kamran Kazemi. “Novel shape-texture feature extraction for medical X-ray image classification.” Int J Innov Comput Inf Control 8 (2012): 659–76.
F. Zhang, Y. M. Yoo, K. L. Mong, Y. Kim, Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction, Medical Imaging, IEEE Transactions on 26 (2) (2007), p. 200–211.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zeelan Basha, C.M.A.K., Maruthi Padmaja, T., Balaji, G.N. (2018). Automatic X-ray Image Classification System. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 78. Springer, Singapore. https://doi.org/10.1007/978-981-10-5547-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-5547-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5546-1
Online ISBN: 978-981-10-5547-8
eBook Packages: EngineeringEngineering (R0)