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Review of Various Fractal Detection Techniques in X-Ray Images
IJCA Proceedings on International Conference on Advancements in Engineering and Technology
© 2015 by IJCA Journal
ICAET 2015 - Number 1
Year of Publication: 2015
Tanudeep Kaur and Anupam Garg. Article: Review of Various Fractal Detection Techniques in X-Ray Images. IJCA Proceedings on International Conference on Advancements in Engineering and Technology ICAET 2015(1):26-28, August 2015. Full text available. BibTeX
@article{key:article,
author = {Tanudeep Kaur and Anupam Garg},
title = {Article: Review of Various Fractal Detection Techniques in X-Ray Images},
journal = {IJCA Proceedings on International Conference on Advancements in Engineering and Technology},
year = {2015},
volume = {ICAET 2015},
number = {1},
pages = {26-28},
month = {August},
note = {Full text available}
}
Abstract
This paper represents the detection and segmentation of X-Ray bone fracture Detection using Edge detection algorithms. Edge detection is applied to find the fracture in a bone in the body (like Skull, Hand, and Leg, wrist, Bar room fracture, Chest, and Spine). Fracture is a medical situation in which there is a separation between two or more pieces of bones. The work of various researchers is discussed about the fractures present in X-Ray images and their detection techniques.
References
- Mahendran, S. K. and Baboo, S. Santosh. (2011) "Automatic Fracture Detection Using Classifiers- A Review" International Journal of Computer Science Issues, Vol. 8, Issue 6, No 1, pp. 340-345.
- Bielecki, A. , Korkosz, M. and Zielinski, B. (2008), "Hand radiographs preprocessing, image representation in the finger regions and joint space width measurements for image interpretation". Pattern Recognition, Vol. 41, Issue. 12, pp. 3786–3798.
- Zielinski, B. "A fully-automated algorithm dedicated to computing metacarpophalangeal and interphalangeal joint cavity widths". Schedae Informaticae, vol. 16, pp. 47–67.
- Mahendran, S. K. and Baboo, S. S. (2011). "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. No. 11, Issue No. 14, Version 1. 0, pp. 23-28.
- Chokkalingam, SP. and Komathy, K. (2014). "Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images". World Academy of Science, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering, Vol. 8, Issue 5, pp. 834-843.
- He, J. C. , Leow, W. K. and Howe, T. S. (2007),"Hierarchical classi?er for detection of fractures in x-ray images". In Computer Analysis of Images and Patterns. Springer, 2007, pp. 962–969
- Y, Jia, and Y. Jiang. (2006) "Active contour model with shape constraints for bone fracture detection. " Computer Graphics, Imaging and Visualization, International Conference on. IEEE, 2006.
- E Jacob, Nathanael. and Wyawahare, M. V. (2013). "Survey of Bone Fracture Detection Techniques". International Journal of Computer Applications, pp. 31-34.
- Montejo-Raez, A. (2005) "Automatic Text Categorization of documents in the High Energy Physics domain," CERN-THESIS-2006-008, ISBN: 84-338-3718-4.
- Linda, C. Harriet, and G. Wiselin Jiji. (2011) "Crack detection in X-ray images using fuzzy index measure. " Applied Soft Computing, Vol, 11, Issue . 4: 3571-3579.