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Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 5 (2021)

DOI
10.11591/ijece.v11i5.pp4050-4058
ISSN
2722-2578, 2088-8708
Published
2021-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

Collaboration network of the authors

27 authors · 92 co-authorship links · 51 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Imam Abdulrahman Bin Faisal University
  • Al-Balqa Applied University
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • Xiamen University Malaysia
  • University of Dubai
  • Jordan University of Science and Technology
  • Saudi Electronic University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Alaa A. AlomoushUniversiti Malaysia Pahang Al-Sultan Abdullah3
Abdul Rahman A. AlsewariAffiliation unknown3
Kamal Z. ZamliUniversiti Malaysia Pahang Al-Sultan Abdullah3
Mohammed AlswaittiXiamen University Malaysia3
Liao ZihaoXiamen University Malaysia3
Mohammad AlauthmanPetra University2
Mutasem K. AlsmadiImam Abdulrahman Bin Faisal University1
Bashar Abedalmohdi AldeebAffiliation unknown1
Hanan Abdullah AlshehriImam Abdulrahman Bin Faisal University1
Shahad Abdulaziz DahdouhImam Abdulrahman Bin Faisal University1
Basstaa Mohammad AlsubaieImam Abdulrahman Bin Faisal University1
Afnan M. AlghamdiImam Abdulrahman Bin Faisal University1
Moath Khaireddin AlkenaniImam Abdulrahman Bin Faisal University1
Amjad GawanmehUniversity of Dubai1
Jamal Nazzal Al-KarakiZayed University1
Ashraf S. MashalehAl-Balqa Applied University1
Noor Farizah IbrahimUniversiti Sains Malaysia1
Shadi AtallaUniversity of Dubai1
Emad Al-MomaniAl Hussein Technical University1
Ala'a ShatnawiJordan University of Science and Technology1
Mohammad AlmomaniJordan University of Science and Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation</dc:title><dc:creator>Alomoush, Waleed</dc:creator><dc:creator>Alrosan, Ayat</dc:creator><dc:creator>Almomani, Ammar</dc:creator><dc:creator>Alissa, Khalid</dc:creator><dc:creator>Khashan, Osama A.</dc:creator><dc:creator>Al-nawasrah, Ahmad</dc:creator><dc:description>&lt;jats:p&gt;Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i5.pp4050-4058</dc:identifier><dc:identifier>10.11591/ijece.v11i5.pp4050-4058</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 5 (2021); 4050</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/25235/15097</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>