Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

Alia, Osama Moh’d; Rajeswari, M.; Ramachandram, Dhanesh; Aziz, Mohd Ezane

doi:10.1109/tencon.2009.5396049

Cited by 22 publications

(21 citation statements)

References 27 publications

(33 reference statements)

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“…It mimics the process of the musicians playing their instruments and attempting to create a well-balanced harmony. It has a soft computing technique that is similar to the genetic algorithm [3] and it has the capacity to exploit our proposed solution vector (harmony vector) with the synchronization of the search space where intensification and diversification environmental optimization are parallel [4].…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

“…The solution procedure of the HS algorithm is controlled using three parameters [4,21]: HMS, HMCR, and PAR. These parameters are defined in the following section.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

“…Through this process, the fitness value of each new vector will be calculated and compared with the worst fitness value in harmony memory [4,28].…”

Section: Fhs-ft Processmentioning

confidence: 99%

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A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

2016

J Inf Process Syst

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In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

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Section: Harmony Search Algorithmmentioning

confidence: 99%

“…The solution procedure of the HS algorithm is controlled using three parameters [4,21]: HMS, HMCR, and PAR. These parameters are defined in the following section.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

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A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

2016

J Inf Process Syst

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“…It has a soft computing technique that is similar to the genetic algorithm [12] and it has capacity to exploit the new solution proposed (harmony) with the synchronization of the search space at a time with intensification and diversification environmental optimization parallel [10].…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

“…Select a value totally random from range possible of values: defined as "randomization". These three rules of HS algorithm are directed by using the three parameters [7,10], which are as follows: Harmony Memory Size (HMS), Harmony Memory Considering Rate (HMCR) and Pitch Adjusting Rate (PAR).…”

Section: Journal Of Computer Sciences and Applicationsmentioning

confidence: 99%

Unsupervised Clustering of Images Using Harmony Search Algorithm

Ibtissem¹,

Fizazi²

2013

JCSA

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Clustering plays an important role in the image processing. It permits to assign a label to each point of the image from a collection of defined classes. Among the domains that use the clustering, we can mention the Remote Sensing for identification of different regions constituting a satellite image. Evaluation of the clustering algorithm results is based on the validity index. In this paper, we applied the Harmony Search algorithm (HS) for make an unsupervised clustering. Thereafter, we evaluated the performance of this tool by analyzing the results obtained. These results show that the validity index determines automatically the appropriate number of classes that represent an image. The study realized with several validity indices allowed us to find the best validity index to evaluate the performance and robustness of the algorithm HS. The experiences obtained with this algorithm show the effectiveness and performance in the stable clustering for given problem.

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Music Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, we introduce a set of music inspired algorithms, namely, harmony search (HS), melody search (MeS) algorithm, and method of musical composition (MMC) algorithm. We first describe the general knowledge of harmony in Sect. 14.1. Then, the fundamentals and performances of HS, MeS algorithm, and MMC algorithm are introduced in Sects. 14.2 and 14.3, respectively. Finally, Sect. 14.4 summarises this chapter.

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Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

Cited by 22 publications

References 27 publications

A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

Unsupervised Clustering of Images Using Harmony Search Algorithm

Music Inspired Algorithms

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