Abstract
Image processing is one of the essential tasks to e xtract suspicious region and robust features from t he Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of bra in 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 convergen ce rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly a lgorithm 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 b rain 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 pixel s in each cluster in the abnormal (multiple scleros is lesions) MRI images. The experimental results prove d the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM cl ustering. 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 cap ability to cluster and segment MRI brain images.
Highlights
Nowadays; in the field of medical image processing research and clinical applications the automatic and dynamic Magnetic Resonance Imaging (MRI) brain segmentation process is still a challenging issueand many researchers are working to resolve this issue (Alia et al, 2011).Generally, this domain deals with the changes in a specific areas in the brain, these areas are the Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM)
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)
A novel efficient and reliable clustering algorithm presented in this work, which is called FIREFLY ALGORITHM BASED FUZZY C-MEAN CLUSTERING (FAFCM) based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm
Summary
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). Alia et al (2011) presented a new dynamic and automatic clustering algorithm for MRI brain image segmentation called DCHS based on hybridization between the Harmony Search with the FCM algorithm. The experimental results indicated that proposed DCHS accurately segmented the multiple tissue categories under serious noise environment and intensity distinctions
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