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To delineate arbitrarily shaped clusters in a complex multimodal feature space, such as the brain MRI intensity space, often requires kernel estimation techniques with locally adaptive bandwidths, such as the adaptive mean shift procedure. Proper selection of the kernel bandwidth is a critical step for a better quality in the clustering. This paper presents a solution for the bandwidth selection,...
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...
To delineate arbitrarily shaped clusters in a complex multimodal feature space, such as the brain MRI intensity space, often requires kernel estimation techniques with locally adaptive bandwidths, such as the adaptive mean shift procedure. Proper selection of the kernel bandwidth is a critical step for a better quality in the clustering. This paper presents a solution for the bandwidth selection,...
In today's buyers' market, "the customer is God", the competition is no longer enterprise to enterprise, but supply chain to supply chain. So exactly to recognize real customer in e-supply chain is the key factor to success for assuring the profit of the whole e-supply chain. There is a large amount of consuming data stored in the e-supply chain everyday. If one e-supply chain can make full...
Cancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells' morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively. Microarray technology...
Functional magnetic resonance imaging (fMRI) studies have shown that different cognitive functions activate overlapping brain regions and the functional responses of the brain differ from region to region. The modified fuzzy c-means (FCM) clustering is used for exploratory analysis of fMRI signals in which the cluster centroids represent typical hemodynamics responses of relatively wide brain regions...
A novel method for segmentation of brain tissues in MRI (magnetic resonance imaging) images is proposed in this paper. First, we reduce noise using a versatile wavelet-based filter. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation...
This paper describes a novel image segmentation algorithm suitable for MRI image segmentation. We introduce a new dissimilarity measure which incorporates the spatial connectivity. A fully automatic technique is developed to obtain the segmentation result and the new clustering objective function incorporates the spatial information. The weighting factor for neighborhood effect is adaptive to the...
A statistical model of the fiber bundles is calculated as the average and standard deviation of a parametric representation of the fiber tracts, using the coefficients of the 3D quintic B-spline representation of the tracts. An atlas of the fiber tracts is constructed by averaging the bundle models over a population with the fiber tracts mapped onto the atlas coordinate. Using the model representation...
Detection of activation regions in functional Magnetic Resonance Imaging (fMRI) experiments can be improved by cluster size thresholding, where small regions are removed in the post-processing step. It is believed that such small regions are spurious and hence can be removed without affecting the truly activated ones. We show that in the context of Markov Random Field (MRF) based segmentation, simple...
Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Clustering medical images for intelligent decision support is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly...
In fMRI dataset, the population of actived voxels is always much less than the total population of the voxels, and that produced an ill-balanced dataset. Some methods, such as limiting the analysis to the gray matter voxels where the BOLD signal is expected and removing the voxels that is absolutely non-actived based on statistical criteria, have been used to treat the ill-balanced dataset. In this...
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