A hybrid Harmony Search algorithm to MRI brain segmentation

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

doi:10.1109/coginf.2010.5599819

Cited by 18 publications

(22 citation statements)

References 52 publications

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“…Because of the complexity of the segmentation process automatic brain image segmentation requires several different approaches, where each approach utilizesdiverse induction ways such as region-based methods (Adams and Bischof, 1994;Alia et al, 2011;Chang and Li, 1994;Pohle and Toennies, 2001;Sijbers et al, 1997), classification-based methods (Bezdek et al, 1993;Dou et al, 2007;Kapur et al, 1996;Mokbel et al, 2000;Szilagyi et al, 2003;Van et al, 1999a;1999b;Wells et al, 1996;Xiaohe et al, 2008;Zhou and Rajapakse, 2008) boundary-based methods (Ashtari et al, 1990;Atkins and Mackiewich, 1998;Ji and Yan, 2002;McInerney and Terzopoulos, 1996) and others in (Beevi and Sathik, 2012;Clark et al, 1997;1998;Shen et al, 2005;Sonka et al, 1996;Cherfa et al, 2007;Zanaty and Aljahdali, 2010;Zhou and Bai, 2007). This intricacyhappens from the intrinsic nature, complicated structures of the MRI brain image (Alia et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays; in the field of medical image processing research and clinical applications (computer-guided surgery, diagnosis of illnesses, tissue volume determination, treatment planning, functional brain mapping, therapy assessment and the anatomical structure studying) the automatic and dynamic MRI brain segmentation process is still a challenging issueand many researchers are working to resolve this issue (Alia et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…So, developing anautomatic approaches is required to increase the volume of the objective brain segmentation (Alia et al, 2011;Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore; any Changes in these tissues volume can be used to characterize the diseases state and entities, such as the diseased tissues characterization (viable tumor, necrotic tissues and edema) (Alia et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…So, this process involves Science Publications AJAS identifying the type of the tissue in each voxel or pixel in 3 dimensional or 2 dimensional datasets according to the previous knowledge and information available from MRI brain images (Alia et al, 2011;Dou et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…So, developing anautomatic approaches is required to increase the volume of the objective brain segmentation (Alia et al, 2011;Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alsmadi¹

2014

American Journal of Applied Sciences

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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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Data Clustering Using Harmony Search Algorithm

Alia

Al-Betar

Rajeswari

et al. 2011

Swarm, Evolutionary, and Memetic Computing

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Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at addressing this problem. The proposed approach consists of two stages. In the first stage, the HS explores the search space of the given dataset to find out the near-optimal cluster centers. The cluster centers found by the HS are then evaluated using reformulated c-means objective function. In the second stage, the best cluster centers found are used as the initial cluster centers for the c-means algorithms. Our experiments show that an HS can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data are experimented with, including the Iris, BUPA liver disorders, Glass, Diabetes, etc. along with two generated data that have several local extrema.

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A hybrid Harmony Search algorithm to MRI brain segmentation

Cited by 18 publications

References 52 publications

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

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

Music Inspired Algorithms

Data Clustering Using Harmony Search Algorithm

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