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

https://doi.org/10.22146/ijccs.81521

Wawan Gunawan(1*), Nurul Latifah(2)

(1) UIN Raden Intan Lampung, Lampung
(2) Master of Biology Education, Muhammadiyah University of Metro, Lampung
(*) Corresponding Author

Abstract


A fuzzy C-Means segmentation algorithm can be implemented in an image segmentation
based on the Mahalanobis distance; However, this method only needs to consider the color
space situation, not the neighborhood system of the image. It was an effective edge detection
process 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 Spatial
information (MFCMS). The proposed method combines feature space and images of the
information of the neighborhood (spatial information) to improve the accuracy of the result of
segmentation on the image. The MFCMS consists of two steps, the histogram threshold module
for the first step and the MFCMS module for the second step. The Histogram Threshold module
is used to get the MFCMS initialization conditions for the cluster centroid and the number of
centroids. Test results show that this method provides better segmentation performance than
classification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,
respectively.

Keywords


Histogram thresholding module; Image Segmentation; Mahalonbis Fuzzy C-Means Spatial Information

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References

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DOI: https://doi.org/10.22146/ijccs.81521

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