Dynamic fuzzy clustering using Harmony Search with application to image segmentation

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

doi:10.1109/isspit.2009.5407590

Cited by 28 publications

(17 citation statements)

References 26 publications

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“…Most of the modern HS variants remove one or more of these parameters from the algorithm completely or replace them with other parameters that are less sensitive to the shape of the search space or are easier to choose. These improved HS variants have been used in many diverse applications and the interested reader is referred to [18][19][20][21][22][23], for examples, many HS variants that are used in computer vision, vehicle routing, music composition, solving Sudoku, and various engineering disciplines.…”

Section: Harmony Searchmentioning

confidence: 99%

Robust Circle Detection Using Harmony Search

Fourie

2017

Journal of Optimization

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Automatic circle detection is an important element of many image processing algorithms. Traditionally the Hough transform has been used to find circular objects in images but more modern approaches that make use of heuristic optimisation techniques have been developed. These are often used in large complex images where the presence of noise or limited computational resources make the Hough transform impractical. Previous research on the use of the Harmony Search (HS) in circle detection showed that HS is an attractive alternative to many of the modern circle detectors based on heuristic optimisers like genetic algorithms and simulated annealing. We propose improvements to this work that enables our algorithm to robustly find multiple circles in larger data sets and still work on realistic images that are heavily corrupted by noisy edges.

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Section: Harmony Searchmentioning

confidence: 99%

Robust Circle Detection Using Harmony Search

Fourie

2017

Journal of Optimization

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“…Generally; these proposed algorithms applied an optimization process (such as particle Swarms and genetic algorithm optimization) as a clustering algorithm with fitness function used for cluster validity index. For further explanation refer to (Alia et al, 2009;Das et al, 2009b;Horta et al, 2009;Hruschka et al, 2006). Alia et al (2011) in spite of the promising results that was obtained from these algorithms, a new metaheuristic algorithm must be developed tosignificantly enhance and improve the accuracy of the segmentation results.…”

Section: Ajasmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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“…Also as a final step and after HS met the stopping criterion, SQP is introduced to the best vector, in term of objective function, stored in HM as a final improvement step. Alia et al (2009c) proposed a new dynamic fuzzy clustering algorithm for image segmentation problems, called Dynamic Clustering Harmony Search (DCHS). DCHS is able to automatically determine the appropriate number of clusters, as well as the appropriate locations of cluster centers.…”

Section: Hybridizing Hs With Other Metaheuristic Componentsmentioning

confidence: 99%

The variants of the harmony search algorithm: an overview

2011

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The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments' pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields especially those working on solving optimization problems. Consequently, this algorithm guided researchers to improve on its performance to be in line with the requirements of the applications being developed. These improvements primarily cover two aspects: (1) improvements in terms of parameters setting, and (2) improvements in terms of hybridizing HS components with other metaheuristic algorithms. This paper presents an overview of these aspects, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.

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Dynamic fuzzy clustering using Harmony Search with application to image segmentation

Cited by 28 publications

References 26 publications

Robust Circle Detection Using Harmony Search

Robust Circle Detection Using Harmony Search

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

The variants of the harmony search algorithm: an overview

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