Image segmentation based on modified centroid weight particle swarm optimization and spatial fuzzy C-means clustering algorithm

Hemalatha, K.; Ranjitha, S.; Suresh, H. N.

doi:10.1109/icatcct.2015.7456978

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…For experimental simulation, MATLAB (version 2015a) was employed on PC with 3.2 GHz with i5 processor. In order to estimate the efficiency of proposed algorithm, the performance of the proposed method was compared with FCM, k-means clustering, and Spatial-FCM (SFCM)-Modified Centroid Weight Particle Swarm Optimization (MPSO) [19] on the reputed database: BraTS. The performance of the proposed methodology was compared in terms of segmentation accuracy and pixel error.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, the proposed segmentation methodology (ABC-ELM-KFCM) is evaluated by considering a sample medical image from BraTS dataset. The effectiveness of the proposed algorithm is verified by comparing with a few existing algorithms like k-means, FCM and SFCM-MPSO algorithm [19]. K.L.…”

Section: Experimental Analysismentioning

confidence: 98%

“…Hemalatha, S. Ranjitha, and H.N. Suresh, [19] proposed spatial FCM clustering algorithm with modified centroid weight particle swarm optimization for image segmentation. In this research paper, the spatial function was summed with membership function in neighbourhood pixels.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Size of portions of an image and segregation of borders leads to build the better segmentation accuracy; the extreme learning algorithm was used in the membership function to learn the patterns of images. The proposed segmentation methodology (ABC-ELM-KFCM) is evaluated by comparing with existing segmentation algorithms such as K Means, FCM, KFCM and SFCM-MPSO [19] in terms of segmentation accuracy and pixel error. For experimental analysis, we considered both medical and multi-media images.…”

Section: Experimental Analysismentioning

confidence: 99%

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Medical Image Segmentation Based on Extreme Learning Machine Algorithm in Kernel Fuzzy C-Means Using Artificial Bee Colony Method

Lingappa¹,

Suresh²,

Manvi³

2018

IJIES

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In image segmentation field, the Fuzzy C-Means (FCM) algorithm is a well-known algorithm for its simplicity and membership function that can control the overlapped clusters effectively with a predefined number of clusters. Despite the fact, the standard FCM algorithm is noise sensitive. To solve the issue, we proposed a new method of clustering named Kernel Fuzzy C-means (KFCM) clustering. KFCM performed well in terms of clustering however, for pattern recognition KFCM has issues. The first one is grouping the similar objects in a single partition due to nonawareness of patterns and the second one is misclassification of data due to the standard structure of the membership subspace plane. Non-awareness of patterns of KFCM is solved by an Extreme Learning Machine (ELM) algorithm and Artificial Bee colony (ABC) algorithm utilized for optimizing the structure of the membership subspace plane. Experimental results showed that the proposed KFCM algorithm performed better segmentation for pattern recognition. At last effectiveness of the proposed algorithm has been evaluated based on comparing the K Means, FCM, spatial FCM, and KFCM algorithms in terms of centroids, segmentation accuracy, and pixel error. The proposed methodology improved the segmentation accuracy up to 0.8-5.5% compared to the existing methods.

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Section: Resultsmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 98%

Section: Experimental Analysismentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

See 2 more Smart Citations