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Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
Image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional...
In this paper, a segmentation technique of multi-spectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Real-coded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted...
In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier descriptors introduce a large number of...
This article describes a multiobjective genetic fuzzy clustering scheme that utilizes the search capabilities of NSGA-II, a popular multiobjective genetic algorithm and optimizes a number of fuzzy cluster validity measures. Real-coded encoding of the cluster centers is used for this purpose. The multiobjective clustering scheme produces a number of non-dominated solutions, each of which contains some...
In this paper, an automatic segmentation technique of multispectral magnetic resonance image of the brain using a new line symmetry based genetic clustering technique is proposed. The proposed real-coded variable string length genetic clustering technique (VGALS clustering) is able to evolve the number of clusters present in the data set automatically. Here assignment of points to different clusters...
Image segmentation is the key step for quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid). Based on genetic algorithm and fuzzy C-means (FCM) approach, a fast and fully automatic segmentation method of brain tissues named genetic fuzzy clustering algorithm is introduced in this paper. The method operates slice by slice based on three main steps: The non-brain...
This paper presents a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. The proposed algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel to be influenced by the labels in its immediate...
This paper presents a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. The proposed algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel to be influenced by the labels in its immediate...
We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.
A novel hierarchical image segmentation approach has been developed for the extraction of tongue carcinoma from magnetic resonance (MR) images. First, a genetic algorithm (GA)-induced fuzzy clustering is used for initial segmentation of MR images of head and neck. Then these segmented masses are refined to reduce the false-positives using an artificial neural network (ANN)-based symmetry detection...
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