Effective algorithm for determining the number of clusters and its application in image segmentation

Pei, Jialun; Zhao, Long; Xiang, Dong; Dong, Xue

doi:10.1007/s10586-017-1083-1

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…The threshold technique is one of the most popular segmentation techniques which are based on the gray level of images. It is simplicity and easy implementation, which has lower computational complexity [ 21 ]. Li et al [ 22 ] presented a novel thresholding extraction method based on variational mode decomposition (VMD).…”

Section: Introductionmentioning

confidence: 99%

A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

Liu

Wang

et al. 2020

Computational Intelligence and Neuroscience

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A new clustering membrane system using a complex chained P system (CCP) based on evolutionary mechanism is designed, developed, implemented, and tested. The purpose of CCP is to solve clustering problems. In CCP, two kinds of evolution rules in different chained membranes are used to enhance the global search ability. The first kind of evolution rules using traditional and modified particle swarm optimization (PSO) clustering techniques are used to evolve the objects. Another based on differential evolution (DE) is introduced to further improve the global search ability. The communication rules are adopted to accelerate the convergence and avoid prematurity. Under the control of evolution-communication mechanism, the CCP can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. This proposed CCP is compared with four existing PSO clustering approaches on eight real-life datasets to verify the validity. The computational results on tested images also clearly show the effectiveness of CCP in solving image segmentation problems.

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Section: Introductionmentioning

confidence: 99%

A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

Liu

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…In practice, to determine the optimal number of clusters several studies have been conducted. Pei et al [13] proposed an optimal method based on the inter-class and intra-class difference to select the number of clusters. Küç ükkülahlı et al [14] performed the segmentation process and selected the optimal number of clusters by examining the histogram both horizontally and vertically to record the local and global maximum values.…”

Section: Introductionmentioning

confidence: 99%

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

Ashour

Guo

Kucukkulahli

et al. 2018

Applied Soft Computing

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In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm.

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A Review of Evolutionary Data Clustering Algorithms for Image Segmentation

Al-Qaisi

Hassonah

Al-Zoubi

et al. 2021

Evolutionary Data Clustering: Algorithms and Applications

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Effective algorithm for determining the number of clusters and its application in image segmentation

Cited by 11 publications

References 20 publications

A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

A Review of Evolutionary Data Clustering Algorithms for Image Segmentation

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