Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution.

Muhammad Moazzam Jawaid; Bushra Naz Soomro; Sajjad Ali Memon; Noor-Ur-Zaman Leghari

doi:10.22581/muet1982.1901.14

Author(s)

Muhammad Moazzam Jawaid Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

Bushra Naz Soomro Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

Sajjad Ali Memon Department of Telecommunication Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

Noor-Ur-Zaman Leghari Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan

Abstract

Accurate segmentation of anatomical organs in medical images is a complex task due to wide interpatient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challeng. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conventional image-based regional intensity statistics, the proposed snake model evolves using composite image energy. Hence, the proposed method offers a greater resistance to the local optima problem as well as initialization perturbations. Experimental results for both synthetic and 2D (Two Dimensional) real clinal images are presented in this work to validate the performance of the proposed method. The performance of the proposed model is evaluated with respect to expert-based manual ground truth.Accordingly, the proposed model achieves higher accuracy in comparison to the state-of-the-art region based segmentation methods of Lankton and Yin as reported in results section.

Publication Details

Page(s) 175-184

DOI 10.22581/muet1982.1901.14

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 38, Issue: 1, Year: 2019

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References

Jawaid , M.M.,Reyes-Aldasoro , C.C., 2017.“Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA Using Mean Radial Profiles”, 144 189 -202

Kass, M., Witkin, A., and Terzopoulos, D., “Snakes: Active Contour Models”, International Journal of Computer Vision, Volume 1, pp. 321-331, 1988

Xu, C., and Prince, J.L., “Snakes, Shapes, and Gradient Vector Flow”, IEEE Transactions on Image Processing, Volume 7, pp. 359-369, 1998

Caselles, V., Kimmel, R., and Sapiro, G., “Geodesic Active Contours”, International Journal of Computer Vision, Volume 22, pp. 61-79, 1997.

Chan, T., and Vese, L., “Active Contours Without Edges”, IEEE Transactions on Image Processing, Volume 10, pp. 266-277, 2001.

Yezzi, A., Tsai, A., and Willsky, A., “A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations”, Journal of Visual Communication and Image Representation, Volume 13, pp. 195-216, 2002.

An, J., Rousson, M., and Xu, C., “-Convergence Approximation to Piecewise Smooth Medical Image Segmentation”, Medical Image Computing and Computer-Assisted Intervention, pp. 495-502, Springer, 2007.

Lankton, S., and Tannenbaum, A., “Localizing Region- Based Active Contours”, IEEE Transactions on Image Processing, Volume 17, pp. 2029-2039, 2008

Li, C., Kao, C.-Y., Gore, J.C., and Ding, Z., “Minimization of Region-Scalable Fitting Energy for Image Segmentation”, IEEE Transactions on Image Processing, Volume 17, pp. 1940-1949, 2008

Wang, Y., and Liatsis, P., “An Automated Method for Segmentation of Coronary Arteries in Coronary CT Imaging”, Developments in E-Systems Engineering, pp.12-16, 2010

Mumford, D., and Shah, J., “Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems”, Communications on Pure and Applied Mathematics, Volume 42, pp. 577–685, 1989.

Li, C., Kao, C.-Y., Gore, J. C., and Ding, Z., “Implicit Active Contours Driven by Local Binary Fitting Energy”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-7, 2007

Sethian, J., “Level Set Methods and Fast Marching Methods”, Cambridge University Press, 1999.

Osher, S., and Sethian, J., “Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations”, Journal of Computational Physics, Volume 79, pp. 12-49, 1988.

Jawaid, M.M., Liatsis, P., Rajani, R., Reyes-Aldasoro, C.C., and SLabaugh, G., “Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA Using Mean Radial Profiles”, Computer Methods and Programs in Biomedicine, Volume 144, pp. 189-202, 2017

Liu, T., Xu, H., Jin, W., Liu, Z., Zhao, Y., and Tian, W., “Medical Image Segmentation Based on a Hybrid Region- Based Active Contour Model”, Computational and Mathematical Methods in Medicine, 2014

Soudani, A., and Zagrouba, E., “Image Segmentation Based on Hybrid Adaptive Active Contour”, Onieva, E., Santos, I., Osaba, E., Quintián, H., and Corchado, E., (Editors), “Hybrid Artificial Intelligent Systems”, Lecture Notes in Computer Science, Volume 9121, Springer, 2015.