Home   >   CSC-OpenAccess Library   >    Manuscript Information
Active Contours Without Edges and Curvature Analysis for Endoscopic Image Classification.
B.V.Dhandra, Ravindra Hegadi
Pages - 19 - 32     |    Revised - 15-06-2007     |    Published - 30-06-2007
Volume - 1   Issue - 1    |    Publication Date - June 2007  Table of Contents
MORE INFORMATION
KEYWORDS
Active Contours, Curvature, Endoscopy, Jacobi method, Level sets
ABSTRACT
Endoscopic images do not contain sharp edges to segment using the traditional segmentation methods for obtaining edges. Therefore, the active contours or ‘snakes’ using level set method with the energy minimization algorithm is adopted here to segment these images. The results obtained from the above segmentation process will be number of segmented regions. The boundary of each region is considered as a curve for further processing. The curvature for each point of this curve is computed considering the support region of each point. The possible presence of abnormality is identified, when curvature of the contour segment between two zero crossings has the opposite curvature signs to those of such neighboring contour segments on the same edge contours. The Knearest neighbor classifier is used to classify the images as normal or abnormal. The experiment based on the proposed method is carried out on 50 normal and 50 abnormal endoscopic images and the results are encouraging.
CITED BY (9)  
1 RAVI, M., & basavaprasad, b. a comparative study on segmentation methods for medical imaging.
2 Jacobs, M., Chang, L. C., Pulkkinen, A., & Romano, M. (2015). Automatic analysis of double coronal mass ejections from coronagraph images. Space Weather, 13(11), 761-777.
3 Lee, g. s. color image segmentation using a morphological gradient-based active contour model.
4 Jacobs, M., Chang, L., & Pulkkinen, A. (2013, January). Automatic Segmentation and Classification of Multiple Coronal Mass Ejections from Coronagraph Images. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The Steering Committee of The World Congress in Computer Science, Compute
5 Dhanalakshmi, M., Sriraam, N., Ramya, G., Bhargavi, N., & Tamizhtennagaarasi, V. (2012). Computer aided diagnosis for enteric lesions in endoscopic images using anfis. International Journal of Wisdom Based Computing, 2(1).
6 Jung, B. K., Wang, W., Li, Z., Son, S. H., & Kim, J. Y. (2012, October). A sectorized object matching approach for breast magnetic resonance image similarity study. In Proceedings of the 2012 ACM Research in Applied Computation Symposium (pp. 172-175). ACM.
7 Anh, N. T. L., Kim, Y. C., & Lee, G. S. (2012, February). Morphological gradient applied to new active contour model for color image segmentation. In Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication (p. 21). ACM.
8 Shikha, d., & dhandra, b. v. abnormality detection in endoscopic images of.
9 D. Shikha and B.V. Dhandra, Abnormality Detection in Endoscopic Images of Throat Cancer by Morphological Operations , Indian Stream Research Journal, 1(4), pp. 1-14, 2011.
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 Socol@r  
9 ResearchGATE 
10 Libsearch 
11 Bielefeld Academic Search Engine (BASE) 
12 Scribd 
13 WorldCat 
14 SlideShare 
15 PDFCAST 
16 PdfSR 
17 Google Books 
B.V.Dhandra, Ravindra Hegadi, Classification of Abnormal Endoscopic Images using Morphological Watershed Segmentation, In Proceedings of International Conference on Cognition and Recognition (ICCR-2005), Mysore, India, 2005
D. Mumford, J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, 42:577-685,1989
Eric W. Weisstein, Jacobi Method, Technical Report
http://digestive.healthcentersonline.com/digestiveimagingtest/endoscopy.cfm
N. Ansari, K.W. Huang, Non-parametric Dominant Point Detection, Pattern Recognition, 24:849-862,1991
P. S. Hiremath, B.V. Dhandra, Iranna Humnabad, Ravindra Hegadi, G.G. Rajput, Detection of esophageal Cancer (Necrosis) in the Endoscopic images using color image segmentation, In Proceedings of second National Conference on Document Analysis and Recognition (NCDAR-2003), Mandya, India, 2003
P. Wang, S. M. Krishnan, C. Kugean, M.P. Tjoa, Classifiation of Endoscopic images based on Texture and Neural Network, In Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Intanbul, Turkey
P.S.Hiremath, B.V.Dhandra, Ravindra Hegadi, G.G.Rajput, Abnormality detection in endoscopic images using color segmentation and curvature computation, In Proceedings of 11th International Conference on Neural Information Processing, ICONIP- 2004, ISI, Calcutta, India, LNCS, ISBN-3-540-23931-6, Springer-Verlag, 2004
S. M. Krishnan, X. Yang, K. L. Chan, S. Kumar, P. M. Y. Goh, Intestinal Abnormality Detection from Endoscopic Images, In Proceedings of 20th Annual International Conference of IEEE EMBS 98, Hongkong, 1998
S. Osher, J. A. Sethian, Front propagating with curvature dependent speed: Algorithm based on Hamilton-Jacobi formulation, Journal of Computational Physics, 79:12-49, 1988
Tony F. Chan, Luminita A. Vese, Active Contours without Edges, IEEE Transactions on Image Processing, 10(2), 2001
V. Torre, T. A. Poggio, On Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:147-163, 1986
Mr. B.V.Dhandra
- India
dhandra_b_v@yahoo.co.in
Mr. Ravindra Hegadi
- India