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An Automated Level Set Segmentation Approach for Lesion Detection in Dental Radiograph for Endodontic Treatment

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Kavindra R. Jain, N. C. Chauhan
10.5120/ijca2017915153

Kavindra R Jain and N C Chauhan. An Automated Level Set Segmentation Approach for Lesion Detection in Dental Radiograph for Endodontic Treatment. International Journal of Computer Applications 172(5):17-24, August 2017. BibTeX

@article{10.5120/ijca2017915153,
	author = {Kavindra R. Jain and N. C. Chauhan},
	title = {An Automated Level Set Segmentation Approach for Lesion Detection in Dental Radiograph for Endodontic Treatment},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {5},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {17-24},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume172/number5/28247-2017915153},
	doi = {10.5120/ijca2017915153},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Medical Imaging is advancing since inception. The engineering and technology with a wide variety of research in segmentation techniques have developed a wide research domain in the field of medical and bioinformatics. Application of segmentation techniques in medical areas for detection of abnormalities has made an add-on approach for both doctors and patients for prior diagnosis and proper treatment. Medical imaging in dental analysis is based on dental radiographs which help the medical practioners in locating hidden dental structures, malignant or benign masses, bone loss, and lesions. An important step during the analysis of dental imaging is extraction of decayed tooth from the dental radiographs. These digital dental radiograph plays a crucial role in detection and further diagnosis of decayed portion in jaw. In this paper, an automated segmentation method using multiphase level set approach is proposed for segmentation of dental radiograph and extraction of region of interest. The extracted region may provide better insight to the medical practitioners during their diagnosis. The results of the proposed segmentation method are analyzed qualitatively and quantitatively and are verified by experts of the domain for various categories.

References

  1. Chunming Li, Rui Huang, Zhaohua Ding, J. Chris Gatenby, “A Level Set Method for Image Segmentation in the Presence of Intensity In-homogeneities With Application to MRI”, in IEEE Transactions on Image Processing  (Volume:20 ,  Issue: 7) ,pp. 2007 – 2016, April 2011.
  2. D. Cremers, “A multiphase levelset framework for variational motion segmentation,” in Proc. Scale Space Meth. Comput. Vis., Isle of Skye, U.K., Jun. 2003, pp. 599–614.
  3. C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, “A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity,” in Proc. Med. Image Comput. Comput. Aided Intervention, 2008, vol. LNCS 5242, pp. 1083–1091, Part II.
  4. T. Chan and L. Vese, “Active contours without edges,” IEEE Trans. Image. Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.
  5. L. Vese and T. Chan, “A multiphase level set framework for image segmentation using the Mumford and Shah model,” Int. J. Comput. Vis., vol. 50, no. 3, pp. 271–293, Dec. 2002.
  6. I Ketut Eddy Purnama, Ima Kurniastuti, Margareta Rinastiti, Mauridhi Hery Purnomo, “Semi-Automatic Determination of Root Canal Length in Dental Dental radiograph Image.
  7. R. B. Tiwari, Prof. A. R. Yardi, “Dental Dental radiograph image enhancement based on human visual system and local image statistics”., International Conference on Image Processing, Computer Vision and Pattern Recognition, 2006, pp 100-108
  8. C. K. Modi , K. J. Pithadiya ,J. D.Chauhan, K. R. Jain, “ Comparative study of Optimal edge detection algorithms for liquid level inspection in Bottles”, International conference on Emerging Trends in Engineering and Technology , pp 447-452, 2009 .
  9. E. H. Said, D. E. M. Nassar, G. Fahmy, H. H. Ammar. "Teeth segmentation in digitized dental Dental radiograph films using mathematical morphology," IEEE Transactions on information forensic and security, vol. 1, Issue. 2, pp. 178-189, June. 2006. 
  10. White & Pharoah “Oral Radiology- Principles and Interpretation”, Fifth Edition (2005), selected illustration by Dr. Donald O’Connor. ISBN 0-323-02001,published by MOSBY (An affiliate of Elsevier)
  11. Shafer’s Tb. “Textbook of Oral Pathology” sixth edition, 2006.
  12. S.A.Ahmed, M.N.Taib, N.E.A.Khalid, R.Ahmad, H.Taib “Performance of compound enhancement algorithms on dental radiograph images” WASET-2011
  13. Stefan Michel, Saskia M.Koller, Markus Ruh, Adrian Schwaninger, “Do “Image Enhancement” Functions Really Enhance Dental radiograph Image Interpretation?” Cognitive Science Journal Archieve 2007
  14. Nirav P. Desai, D. B. Prajapati “A simple and novel CBIR technique for features extraction using AM dental radiographs” CSNT (IEEE) 2013, Gwalior, pp.198-202, 2012
  15. Maja Omanovic, Jeff J. Orchard “Exhaustive Matching of Dental Dental radiographs for Human Forensic Identification “Journal of the Canadian Society of Forensic Science, 2008
  16. S.Jadhav, R. Shriram “Dental biometrics used in forensic science” IJERS/Vol.III/ Issue I/January-March, 2012/26-29
  17. Omaima Nomir, M.A.Mottaleb “A system for human identification from Dental radiograph dental radiographs” Pattern Recognition 38 (2005) 1295 - 1305.
  18. Sharifah Lailee, Nursuriati Jamil “Segmentation of Natural Images Using an Improved Thresholding-based Technique” IRIS 2012, Elsevier Procedia engg. Conference pp938-944.
  19. TM Lehmann, E.Troeltsch, K Spitzer” Image processing and enhancement provided by commercial dental software programs: A Technical Report” Dentomaxillofacial Radiology (2002) 31, 264-272.
  20. T. N. Cornsweet, Visual Perception, Academic Press, New York, 1970.
  21. Yan Gao and Jesse S. Jin, “Fast LoG Filtering using Recursive Filters”, IEEE Trans., pp.133 -138,1995.
  22. Osher S, Fedkiw R (2006) Level set methods and dynamic implicit surfaces, vol 153. Springer Science & Business Media.
  23. Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175
  24. Weickert J, Kuhne G (2003) Fast methods for implicit active contour models. In: Geometric level set methods in imaging, vision, and graphics. Springer, pp 43–57.
  25. Shuo L, Fevens T, Krzyzak A, Li S (2006) An automatic variational level set segmentation framework for computer aided dental dental radiographs analysis in clinical environments. Comput Med Imaging Graph 30(2):65–74.
  26. Qu Y, Wong TT, Heng PA (2007) Image segmentation using the level set method. In: Deformable models. Springer, pp 95–122.

Keywords

Image-Segmentation; Intensity-Inhomogeneity; Level Set; Root Canal Treatment (RCT); lesion.