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Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 2 (2023) · pp. 139–150

DOI
10.22146/ijccs.81521
ISSN
2460-7258, 1978-1520
Published
2023-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.

Keywords

Collaboration network of the authors

5 authors · 7 co-authorship links · 6 across different organisations

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

  • Lampung University
  • Universitas Islam Negeri Raden Intan Lampung
  • Universitas Muhammadiyah Metro
  • Institut Teknologi Sepuluh Nopember
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
⁠Undang RosidinLampung University1
Nina KadaritnaLampung University1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation</dc:title>
	<dc:creator>Gunawan, Wawan</dc:creator>
	<dc:creator>Latifah, Nurul</dc:creator>
	<dc:subject xml:lang="en-US">Image Processing</dc:subject>
	<dc:subject xml:lang="en-US">Histogram thresholding module; Image Segmentation; Mahalonbis Fuzzy C-Means Spatial Information</dc:subject>
	<dc:description xml:lang="en-US">A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Lembaga Penelitian dan Pengabdian kepada Masyarakata (LP2M) UIN Raden Intan Lampung</dc:contributor>
	<dc:date>2023-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/81521</dc:identifier>
	<dc:identifier>10.22146/ijccs.81521</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 2 (2023): April; 139-150</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/81521/36331</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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