Abstract
The fundamental task in the case of medical images is detection of interested region(s) and their further investigations either in an automated fashion or through expert interventions. The presence of noise and image anomalies lead to imperfection in boundary detection as it depends on intensity and contrast. Basic image processing techniques/tools used priorly help the practioners for proper selction of region of interest. Sometimes it was observed during experimentation that regionof interest so selected by the edge detection algorithm in the tool are not clear and proper discretisation of the regions were not exact as desired.. So proper image segmentation for extraction of features is essential. To forfeit the above conditions partitioning of an image into subsections must satisfy certain conditions. Here, in the proposed snakes-based approach we took the points directly so that it can automatically extract the affected part. An entire interactive tool with GUI was developed in MATLAB and used to generate all results shown in this chapter. In addition to snakes model, some of the basic features like area and canny were also included in the GUI-based tool. This interactive user friendly approach can help practitioners in identification of region of interest which can be further used for a precancerous treatment. It was observed that cases with precancerous treatment under erythroplakia and leucoplakia were satisfactorily diagnosed and that results obtained were satisfactory for helping out the practitioners too.
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
Bibliography
Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29(1), 100–132.
Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 898–916.
Batchelor, B. G. (1994). Edge-region-based segmentation of range images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(3), 314–319.
Kaganami, H. G., & Beiji, Z. (2009). Region-based segmentation versus edge detection. In IIH-MSP 2009 – 2009 5th international conference on intelligent information hiding and multimedia signal processing, pp. 1217–1221.
Muthukrishnan, R., & Radha, M. (2011). Edge detection techniques for image segmentation. International Journal of Computer Science, 3(6), 259–267.
Kass, M., Witkin, A., & Terzopoulos, D. (1987). Snakes: Active contour models. International Journal of Computer Vision, 1, 321–331.
Xu, J., Chutatape, O., & Chew, P. (2007). Automated optic disk boundary detection by modified active contour model. IEEE Transactions on Biomedical Engineering, 54(3), 473–482.
Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J. P., & Osher, S. (2007). Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision, 28(2), 151–167.
Derraz, F., Beladgham, M., & Khelif, M. (2004, May). Application of active contour models in medical image segmentation. In International conference on information technology: Coding and computing, 2004. Proceedings. ITCC 2004, vol. 2, pp. 675–681.
Papandreou, G., & Maragos, P. (2007). Multigrid geometric active contour models. IEEE Transactions on Image Processing, 16(1), 229–240.
Fischer, B., & Modersitzki, J. (2003). Curvature based image registration. Journal of Mathematical Imaging and Vision, 18(1), 81–85.
Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P., & Tannenbaum, A. (1997). A geometric snake model for segmentation of medical imagery. IEEE Transactions on Medical Imaging, 16(2), 199–209.
Wang, G., & Wang, D. (2010). Segmentation of brain MRI image with GVF Snake model. In Proceedings – 2010 1st international conference on pervasive computing, signal processing and applications, PCSPA 2010, pp. 711–714.
Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using Matlab – Gonzalez Woods & Eddins.pdf. Education, 624(2), 609.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Jain, K.R., Chauhan, N.C. (2019). Segmentation of Dental Radiographs Using Active Contour Model. In: Dental Image Analysis for Disease Diagnosis. Springer, Cham. https://doi.org/10.1007/978-3-030-14136-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-14136-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14135-6
Online ISBN: 978-3-030-14136-3
eBook Packages: EngineeringEngineering (R0)