Membrane Computing for Real Medical Image Segmentation

  • Rafaa I. Yahya (1) Department of Computer, College of Science, Mustansiriyah University, Baghdad. (2) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research. Universiti Teknologi Malaysia. http://orcid.org/0000-0003-0375-4556
  • Siti Mariyam Shamsuddin UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research. Universiti Teknologi Malaysia.
  • Salah I. Yahya Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region - F.R. Iraq. (2) Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Kurdistan Region - F.R. Iraq. (3) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, Malaysia http://orcid.org/0000-0002-2724-5118
  • Bisan Alsalibi (1) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor. (2) School of Computer Sciences, Universiti Sains Malaysia, USM,
  • Ghada K. Al-Khafaji (1) Department of Computer, College of Science, University of Baghdad, Baghdad, Iraq. (2) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, UTM Skudai, Malaysia.
DOI: https://doi.org/10.14500/aro.10442
Keywords: Edge-based segmentation, medical images, membrane computing, P-Lingua, region-based segmentation, tissue-like P system

Abstract

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels of 8-adjacency give better results compared with the 4-adjacency neighborhood relations, because the 8-adjacency considers the eight pixels around the center pixel, which reduces the required communication rules to obtain the final segmentation results. The experimental results proved that the proposed approach has superior results in terms of the number of computational steps and processing time. To the best of our knowledge, this is the first time an evaluation procedure is conducted to evaluate the efficiency of real image segmentations using membrane computing.

Downloads

Download data is not yet available.

Author Biographies

Rafaa I. Yahya, (1) Department of Computer, College of Science, Mustansiriyah University, Baghdad. (2) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research. Universiti Teknologi Malaysia.
Rafaa I. Yahya received her B.Sc. degree in Computer Science from Al-Rafidain University College in Baghdad, Iraq, and her M.Sc. in computer science (image processing) from Mustansiriyah University in Baghdad, Iraq. Currently, she is working toward her Ph.D. degree in computer science (Bioinformatics) from Universiti Teknologi Malaysia, Johor, Malaysia. Her research interests include bioinformatics, membrane computing, and image processing.
Siti Mariyam Shamsuddin, UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research. Universiti Teknologi Malaysia.
Siti Mariyam Shamsuddin received her B.Sc. and M.Sc. degrees in mathematics from New Jersey and her Ph.D. degree in pattern recognition and artificial intelligence from Universiti Putra Malaysia, Malaysia. Currently, she is the Director of the UTM Big Data Center and a full professor of computer science at Universiti Teknologi Malaysia, Johor, Malaysia. Her research interests include big data analytics, machine learning, GPU computing, soft computing and its applications, pattern recognition, and geometric modelling.
Salah I. Yahya, Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region - F.R. Iraq. (2) Department of Computer Science and Engineering, School of Science and Engineering, University of Kurdistan Hewler, Kurdistan Region - F.R. Iraq. (3) UTM Big Data Center, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, Malaysia

Salah I. Yahya is a Professor, joined the Department of Software Engineering at Koya University in 2010. He has a B.Sc. degree in Electrical Engineering, M.Sc. degree in Electronics and Communication Engineering and Ph.D. degree in Communication and Microwave Engineering. He is a Consultant at the Iraqi Engineering Union. Dr. Yahya has many scientific publications; (2) books, (14) Journal Articles and more than (32) conference papers. He is a senior member of the IEEE-USA and a member of AMTA-USA, SDIWC-Hong Kong. Dr. Yahya is a regular reviewer of the Electromagnetics Academy, Cambridge, USA, PIERS Journalspublications, since 2009, Science and Engineering of Composite Materials journal and International Journal of Applied Electromagnetics and Mechanics, as well as, a regular reviewer of SDIWC conferences. His h-index is (7). [Click to see Academic Profile]

References

Alsalibi, B., Venkat, I., Subramanian, K. and Christinal, H., 2014. A Bio-Inspired Software for Homology Groups of 2D Digital Images. Asian Conference on Membrane Computing ACMC 2014, Coimbatore, pp.1-4.

Carnero, J., Dıaz-Pernil, D. and Gutiérrez-Naranjo, M.A., 2011. Designing tissue-like P systems for image segmentation on parallel architectures. Ninth Brainstorming Week on Membrane Computing, 2011, pp.43-62.

Carnero, J., Díaz-Pernil, D., Molina-Abril, H. and Real, P., 2010. Image segmentation inspired by cellular models using hardware programming. 3rd International Workshop on Computational Topology in Image Context, 1(3), pp.143-150.

Christinal, H.A., Díaz-Pernil, D. and Jurado, P.R., 2009. Segmentation in 2D and 3D image using tissue-like P system. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer, Switzerland, pp.169-176.

Christinal, H.A., DíAz-Pernil, D. and Real, P., 2010. Psystems and computational algebraic topology. Mathematical and Computer Modelling, 52(11), pp.1982-1996.

Christinal, H.A., Díaz-Pernil, D. and Real, P., 2011. Region-based segmentation of 2D and 3D images with tissue-like P systems. Pattern Recognition Letters, 32(16), pp.2206-2212.

Christinal, H.A., Dıaz-Pernil, D., Gutiérrez-Naranjo, M.A. and PérezJiménez, M.J., 2010. Thresholding of 2D images with cell-like P systems. Romanian Journal of Information Science and Technology (ROMJIST), 13(2), pp.131-140.

Christinal, H.A., Díaz-Pernil, D., Jurado, P.R. and Selvan, S.E., 2012. Color segmentation of 2D images with thresholding. Eco-friendly Computing and Communication Systems, 305, pp.162-169.

Computer Vision Group. 2013. Contour Detectionand Image Segmentation Resources. University of California, Berkely. Available from: http://www. eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html. [Last accessed on 2016 Jan 10].

Díaz-Pernil, D., Berciano, A., Peña-Cantillana, F. and Gutiérrez-Naranjo, M.A., 2013. Segmenting images with gradient-based edge detection using membrane computing. Pattern Recognition Letters, 34(8), pp.846-855.

Díaz-Pernil, D., Gutiérrez-Naranjo, M.A., Molina-Abril, H. and Real, P., 2012. Designing a new software tool for digital imagery based on P systems. Natural Computing, 11(3), pp.381-386.

Diaz-Pernil, D., Gutiérrez-Naranjo, M.A., Real, P. and Sánchez-Canales, V., 2010. Computing homology groups in binary 2D imagery by tissue-like P systems. Romanian Journal of Information Science and Technology, 13(2), pp.141-152.

Díaz-Pernil, D., Molina-Abril, H., Real, P. and Gutiérrez-Naranjo, M., 2010. A bio-inspired software for segmenting digital images. Proceeding of the 2010 IEEE fifth international conference on Bio-inspired computing theories and applications BIC_TA, computer. Society, 2, pp.1377-1381.

Garcıa-Quismondo, M., Gutiérrez-Escudero, R., Pérez-Hurtado, I. and Pérez-Jiménez, M.J., 2009. P-lingua 2.0: New features and first applications. Proceedings of the Seventh Brainstorming Week on Membrane Computing, Sevilla, Spain, 1, pp.141-167.

Ionescu, M., Paun, G. and Yokomori, T., 2006. Spiking neural P systems. Fundamenta Informaticae, 71(2), pp.279-308.

Isawasan, P., Venkat, I., Subramanian, K., Khader, A., Osman, O. and Christinal, H., 2014. Region-based segmentation of Hexagonal digital images using membrane computing. Asian Conference on Membrane Computing (ACMC), pp.1-4.

Martı, C., Păun, G. and Pazos, J., 2003. Tissue P systems. Theoretical Computer Science, 296(2), pp.295-326.

Martin-Vide, C., Pazos, J., Păun, G. and Rodríguez-Patón, A., 2002. A new class of symbolic abstract neural nets: Tissue P systems. Computing and Combinatorics, 2327, pp.290-299.

Păun, G. and Rozenberg, G., 2002. Aguide to membrane computing. Theoretical Computer Science, Elsevier, 287(1), pp.73-100.

Păun, G., 2000. Computing with membranes. Journal of Computer and System Sciences, 61(1), pp.108-143. Păun, G., 2002. Introduction: Membrane computing—what it is and what it is not. Membrane Computing. Springer, London, pp.1-6.

Peña-Cantillana, F., Díaz-Pernil, D., Berciano, A. and Gutiérrez-Naranjo, M.A., 2011. A parallel implementation of the thresholding problem by using tissuelike P systems. International Conference on Computer Analysis of Images and Patterns, 29, pp.277-284.

Pena-Cantillana, F., Dıaz-Pernil, D., Christinal, H.A. and Gutiérrez-Naranjo,M.A., 2011. Smoothing problem in 2D images with tissue-like P systems and parallel implementation. Proceedings of the Ninth Brainstorming Week on Membrane Computing, Fénix Editora, pp.317-328.

Peng, H., Shao, J., Li, B., Wang, J., Pérez-Jiménez, M.J., Jiang, Y. and Yang, Y. 2012. Image thresholding with cell-like P systems. Proceedings of the Tenth Brainstorming Week on Membrane Computing, 2, p.3.

Peng, H., Wang, J. and Pérez-Jiménez, M.J., 2015. Optimal multi-level thresholding with membrane computing. Digital Signal Processing, 37, pp.53-64.

Peng, H., Yang, Y., Zhang, J., Huang, X. and Wang, J., 2014. Aregion-based color image segmentation method based on P systems. Science and Technology, 17(1), pp.63-75.

Rahtu, E., Kannala, J., Salo, M. and Heikkila, J., 2010. Segmenting salient objects from images and videos. ECCV, pp.366-379. Available from: http://www.cg.cs. tsinghua.edu.cn/people/~cmm/saliency2/.

Reina-Molina, R., Carnero, J. and Diaz-Pernil, D., 2010. Image segmentation using tissue-like P systems with multiple auxiliary cells. Image-A, 1(3), pp.143-150.

Shapiro, L. and Stockman, G.C., 2001. Computer Vision. 2001 ed. Prentice Hall, Englewood.

Cliffs, NJ. Sheeba, F., Thamburaj, R., Nagar, A.K. and Mammen, J.J., 2011. Segmentation of Peripheral Blood Smear Images using Tissue-Like P Systems. Bio-Inspired Computing: Theories and Applications (BIC-TA), 2011 Sixth International Conference on, IEEE, pp.257-261.

Shi, R., Ngan, K.N. and Li, S., 2014. Jaccard index compensation for object segmentation evaluation. IEEE International Conference on Image Processing, 71, pp.4457-4461.

Yahya, R.I., Hasan, S., George, L.E. and Alsalibi, B., 2015. Membrane computing for 2D image segmentation. International Journal of Advances in Soft Computing and its Applications, 7(1), pp.1-15.

Yahya, R.I., Shamsuddin, S.M., Yahya, S.I., Hasan, S., Al-Salibi, B. and Al-Khafaji, G., 2016. Image segmentation using membrane computing: A literature Survey. Bio-Inspired Computing-Theories and Applications. Springer, Singapore, pp.314-335.

Yahya, R.I., Shamsuddin, S.M., Hasan, S. and Yahya, S.I., 2016. Tissue-like P system for Segmentation of 2D hexagonal images. ARO-The Scientific Journal of Koya University, 4(1), pp.35-42. DOI: http://dx.doi.org/10.14500/aro.10135

Yang, Y., Peng, H., Jiang, Y., Huang, X. and Zhang, J., 2013. A region-based image segmentation method under P systems. Journal Information Computer Sciences, 10(10), pp.2943-2950.

Zhang, Z. and Peng, H., 2012. Object segmentation with membrane computing. Journal of Information and Computational Science, 9(17), pp.5417-5424.

ARO Journal: Volume 6, No. 2 (2018)
Published
2018-12-10
How to Cite
Yahya, R. I., Shamsuddin, S. M., Yahya, S. I., Alsalibi, B. and Al-Khafaji, G. K. (2018) “Membrane Computing for Real Medical Image Segmentation”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 6(2), pp. 27-38. doi: 10.14500/aro.10442.
Issue
Vol. 6 No. 2 (2018): Issue Eleven
Section
Articles

Copyright (c) 2018 Rafaa I. Yahya, Siti Mariyam Shamsuddin, Salah I. Yahya, Bisan Alsalibi, Ghada K. Al-Khafaji

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who choose to publish their work with Aro agree to the following terms:

  • Authors retain the copyright to their work and grant the journal the right of first publication. The work is simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0]. This license allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors have the freedom to enter into separate agreements for the non-exclusive distribution of the journal's published version of the work. This includes options such as posting it to an institutional repository or publishing it in a book, as long as proper acknowledgement is given to its initial publication in this journal.

  • Authors are encouraged to share and post their work online, including in institutional repositories or on their personal websites, both prior to and during the submission process. This practice can lead to productive exchanges and increase the visibility and citation of the published work.

By agreeing to these terms, authors acknowledge the importance of open access and the benefits it brings to the scholarly community.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)