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This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and connecting contents from background. Specifically, local GMM is firstly applied to detect nuclei by scanning the input image, which is followed...
This paper presents an apple recognition method based on texture features and Maximum Expectation (EM) algorithm for Gaussian Mixture Model (GMM). The images were converted to HSV space from RGB space and the H channel images were selected as interested images to be processed. The images of H channel were divided into blocks of 8*8 pixels and the texture features of the blocks were calculated. Angular...
A novel remote-sensing image segmentation method is presented in the framework of Normalized Cuts to solve the perceptual grouping problem by means of graph partitioning. In this method, texton is applied to obtain color features and texture features of remote-sensing image. Clustering of the original color values and the filter responses of the images is performed to find texton. The filter bank...
Lip feature extraction is one of the most challenging tasks in the lip reading systems' performance. In this paper, a new approach for lip contour extraction based on fuzzy clustering is proposed. The algorithm employs a stochastic cost function to partition a color image into lip and non-lip regions such that the joint probability of the two regions is maximized. First, the mouth location is determined...
This paper presents a work on accurate image segmentation utilizing local image characteristics. Image features are measured by employing an appropriate Gabor filter with adaptively chosen size, orientation, frequency and phase for each pixel. An image property called phase divergence is used for the selection of the appropriate filter size. Characteristic features related to the change in brightness,...
In this paper fuzzy clustering algorithms are utilized for the segmentation of hyperspectral images. For this purpose fuzzy c-means and an extended version of this algorithm, namely the fuzzy Gustafson-Kessel algorithms are used. Because of the high dimensionality in hyperspectral images, the data dimension is reduced using the Discrete Wavelet Transform. The advantage of using fuzzy approaches for...
A novel and more effective algorithm used for segmenting pulmonary nodules in thoracic spiral CT images was presented. The algorithm is based on mean shift clustering method and CI (Convergence Index) features, which can represent the multiple Gaussian model of pulmonary nodules both for solid and sub-solid, substantially. The algorithm has the following steps: (1) calculating the CI features of all...
We present an automatic segmentation and its visualization method for medical image. First, the statistical segmentation consists of two steps: number detection of clusters composing an image and parameter estimation of a statistical model. Here we use the morphological operations to determine automatically the number of clusters or objects composing a given image without any prior knowledge and adopt...
Image clustering can be viewed as a segmentation problem in which small image patches are grouped together based on their features. Rock texture segmentation is a challenging task since the texture is often nonhomogeneous. In this contribution, the new EM (expectation-maximization) rock textures segmentation framework EMRT is proposed. EMRT has two phases, in the first phase the image is divided into...
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