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In the paper, a rough spatial kernelized fuzzy c-means clustering (RSKFCM) based medical image segmentation algorithm is proposed. This technique is a combination of rough set and spatial kernelized fuzzy c-means clustering (SKFCM). SKFCM is failed to remove the indistinct knowledge that is associated with each data set during the process of its assignment to a particular cluster. The rough set is...
Image segmentation is an important problem in image processing and object recognition, and is one well-known bottleneck for further applications. Fuzzy C-means, as one typical clustering algorithm in pattern recognition, has been improved for image segmentation in many aspects. Aiming at the distance form in FCM, this paper proposes to incorporate FCM with kernel functions, which will make it insensitive...
Image segmentation has key influence in numerous medical imaging uses. In this paper, we present a new algorithm for spatial fuzzy segmentation using modified particle swarm optimization of medical & multimedia data. The algorithm is realized by modifying the scaling parameters in the conventional fuzzy C-means (FCM) algorithm using Modified Particle Swarm Optimization (MPSO). Spatial coordinates...
Medical image segmentation is a fundamental preprocessing step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means clustering algorithm performs well in the absence of noise. Traditional FCM leads to its non robust result mainly due to 1. Not utilizing the spatial information in the image. 2. Use of Euclidean distance. These limitations can be...
In order to apply successfully the fuzzy clustering algorithms like shadowed C-means (SCM) to image segmentation problems, the spatial information related with each pixel in the image should be carefully calculated and appended to the clustering algorithms. In this paper, the non-local spatial information calculation is introduced to SCM. Because the data in the kernel space demonstrate more linearly-separable...
In this paper, a suitable novel algorithm has been proposed for segmenting the brain magnetic resonance imaging (MRI) data using an efficient kernelized fuzzy c-means (EKFCM) with spatial constraints. In this proposed algorithm, the Euclidean distance in the standard fuzzy c-means (FCM) is replaced by a Gaussian radial basis function with additive bias. The proposed method will segment the given MRI...
In this study, we introduce a new directional nonparametric clustering algorithm for 3D medical structure topology classification. This paper proposes directional mean shift (DMS) which extends the well known mean shift-based clustering, for handling directional statistics, toward analyzing directional/circular-domain data with phase-wraparound boundary conditions. Our overall approach transforms...
This paper presents a study aimed to the realization of a novel multiresolution registration framework. The transformation function is computed iteratively as a composition of local deformations determined by the maximization of mutual information. At each iteration, local transformations are joint together using fuzzy kernel regression. This technique represents the core of the method and it's formally...
Limited spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities variation make the assignment of segmentation of medical images is greatly difficult. Mathematical algorithm supported automatic segmentation system plays an important role in segmentation of medical imaging. This paper presents an effective fuzzy segmentation algorithm for breast magnetic resonance...
A Gaussian kernel-based fuzzy clustering algorithm with spatial constraints for automatic segmentation of lung field in chest radiographs is proposed in this paper. The algorithm is realized by modifying the objective function in the conventional fuzzy c-means algorithm using Gaussian kernel-induced distance metric. The influence of the neighboring pixels on the centre pixel in chest radiograph was...
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