MRI brain segmentation via hybrid firefly search algorithm

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Magnetic resonance imaging (MRI) brain tumor segmentation is a challenging tasks which include the detection task of tumor from images. In general, this process is done manually by experts in medical images field which is always unclear, because the similarity between normal and abnormal tissues. The present study proposes a new clustering approach based on the hybridization of firefly algorithm (FA) and fuzzy c-means algorithm(FCM) called (FFCM) to segment MRI brain images. this approach use the capability of firefly search to find optimal initial cluster centres for the FCM and thus improve (MRI) brain tumor segmentation. The proposed approach was evaluated by applying it to a magnetic resonance imaging (MRI) brain segmentation problem using a simulated brain data set of McGill University and real MRI images from Internet Brain Segmentation Repository benchmark data sets. The cluster validity index (Rm) was used as a fitness function to determine the best solutions obtained by the firefly algorithm. The experiments indicated encouraging results after applying FFCM, compared with the outcomes of state-of-the-art segmentation algorithms and FCM random initialization of cluster centres.

Original languageEnglish
Pages (from-to)73-90
Number of pages18
JournalJournal of Theoretical and Applied Information Technology
Volume61
Issue number1
Publication statusPublished - 2014

Fingerprint

Magnetic Resonance Imaging
Hybrid Algorithm
Magnetic resonance imaging
Search Algorithm
Brain
Segmentation
Fuzzy C-means Algorithm
Brain Tumor
Tumors
Cluster Validity Index
Medical Image
Fitness Function
Initialization
Repository
Magnetic resonance
Tumor
Clustering
Benchmark
Internet
Tissue

Keywords

  • Firefly algorithm
  • Fuzzy c-means
  • MRI images segmentation
  • Rm validity index

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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abstract = "Magnetic resonance imaging (MRI) brain tumor segmentation is a challenging tasks which include the detection task of tumor from images. In general, this process is done manually by experts in medical images field which is always unclear, because the similarity between normal and abnormal tissues. The present study proposes a new clustering approach based on the hybridization of firefly algorithm (FA) and fuzzy c-means algorithm(FCM) called (FFCM) to segment MRI brain images. this approach use the capability of firefly search to find optimal initial cluster centres for the FCM and thus improve (MRI) brain tumor segmentation. The proposed approach was evaluated by applying it to a magnetic resonance imaging (MRI) brain segmentation problem using a simulated brain data set of McGill University and real MRI images from Internet Brain Segmentation Repository benchmark data sets. The cluster validity index (Rm) was used as a fitness function to determine the best solutions obtained by the firefly algorithm. The experiments indicated encouraging results after applying FFCM, compared with the outcomes of state-of-the-art segmentation algorithms and FCM random initialization of cluster centres.",
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AU - Sheikh Abdullah, Siti Norul Huda

AU - Sahran, Shahnorbanun

AU - Iqbal Hussain, Rizuana

PY - 2014

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N2 - Magnetic resonance imaging (MRI) brain tumor segmentation is a challenging tasks which include the detection task of tumor from images. In general, this process is done manually by experts in medical images field which is always unclear, because the similarity between normal and abnormal tissues. The present study proposes a new clustering approach based on the hybridization of firefly algorithm (FA) and fuzzy c-means algorithm(FCM) called (FFCM) to segment MRI brain images. this approach use the capability of firefly search to find optimal initial cluster centres for the FCM and thus improve (MRI) brain tumor segmentation. The proposed approach was evaluated by applying it to a magnetic resonance imaging (MRI) brain segmentation problem using a simulated brain data set of McGill University and real MRI images from Internet Brain Segmentation Repository benchmark data sets. The cluster validity index (Rm) was used as a fitness function to determine the best solutions obtained by the firefly algorithm. The experiments indicated encouraging results after applying FFCM, compared with the outcomes of state-of-the-art segmentation algorithms and FCM random initialization of cluster centres.

AB - Magnetic resonance imaging (MRI) brain tumor segmentation is a challenging tasks which include the detection task of tumor from images. In general, this process is done manually by experts in medical images field which is always unclear, because the similarity between normal and abnormal tissues. The present study proposes a new clustering approach based on the hybridization of firefly algorithm (FA) and fuzzy c-means algorithm(FCM) called (FFCM) to segment MRI brain images. this approach use the capability of firefly search to find optimal initial cluster centres for the FCM and thus improve (MRI) brain tumor segmentation. The proposed approach was evaluated by applying it to a magnetic resonance imaging (MRI) brain segmentation problem using a simulated brain data set of McGill University and real MRI images from Internet Brain Segmentation Repository benchmark data sets. The cluster validity index (Rm) was used as a fitness function to determine the best solutions obtained by the firefly algorithm. The experiments indicated encouraging results after applying FFCM, compared with the outcomes of state-of-the-art segmentation algorithms and FCM random initialization of cluster centres.

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