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首页> 外文期刊>American journal of applied sciences >A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION
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A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION

机译：混合模糊算法与模糊C均值算法在MRI脑分割中的应用

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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.
机译：图像处理是从磁共振成像（MRI）中提取可疑区域和鲁棒特征的基本任务之一。为了满足并提高脑肿瘤检测的准确性，开发了许多分割算法。在医学图像处理中，脑图像分割被认为是复杂且具有挑战性的部分。 Fuzzy c-means是一种无监督方法，已实现用于MRI的聚类和不同目的，例如识别感兴趣的模式和图像分割。然而;模糊c均值算法仍然存在许多缺点，例如收敛速度慢，陷入局部极小并且容易受到初始化敏感性的影响。 Firefly算法是一种新的基于种群的优化方法，已成功用于解决许多复杂问题。本文提出了一种将萤火虫算法（FA）与模糊C均值算法（FCM）混合使用的动态智能聚类方法，用于脑肿瘤的分割。为了自动分割MRI脑图像并提高FCM的能力，以自动得出异常（多发性硬化病灶）MRI图像中簇中心的正确数量和位置以及每个簇中的像素数量。实验结果证明了所提出的FAFCM在增强传统FCM聚类性能方面的有效性。此外;定性和定量地显示了FAFCM与其他最新细分方法的优势。结论：本文提出了一种新的高效可靠的聚类算法，即基于萤火虫算法与模糊c均值聚类算法混合的FAFCM。自动；混合算法能够对MRI脑图像进行聚类和分割。

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