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.
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.

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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]

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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.

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