首页> 外文期刊>International journal of imaging systems and technology >Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed-based region growing algorithm
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Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed-based region growing algorithm

机译:基于种子区域生长算法的优化和模糊聚类相结合,在T1,T2,FLAIR和MPR脑图像中进行肿瘤检测

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摘要

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 - w, T2 - w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C - Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogene ous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentat ion in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. (C) 2017 Wiley Periodicals, Inc.
机译:可以使用与基于种子的区域生长算法合并的优化和聚类对磁共振(MR)脑图像中存在的肿瘤和水肿区域进行分割。所提出的算法在T1-w,T2-w,流体衰减反转恢复和多平面重建型MR脑图像中显示出对肿瘤检测的有效性。在通过改进粒子群优化(MPSO)和模糊C均值(FCM)算法显示出初始级别分割之后,使用区域增长算法并基于这些种子点来初始化种子点。 MR脑图像中的肿瘤检测已完成。用于与常规技术进行比较的参数是均方误差,峰值信噪比,Jaccard(Tanimoto)指数,Dice Overlap指数和计算时间。这些参数证明了该算法的有效性。使用提出的算法对输入MR脑图像中存在的异质类型肿瘤区域进行分割。此外,该算法显示了在脑肿瘤识别过程中的增强。黄金标准图像的可用性已导致将建议的算法与基于MPSO的FCM和常规的区域增长算法进行比较。而且,这项研究推荐的算法能够产生0.96的相似度指数值,0.97的重叠分数值和0.05的额外分数值,这远远好于基于MPSO的FCM和“区域增长”算法所阐明的值。所提出的算法有利于对比度增强图像的分割。 (C)2017威利期刊公司

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