A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.
TLDR
A segmentation tool is presented in order to differentiate the anatomical structures within the vectorial volume of the CT uroscan to get a better classification result and is less affected by the noise.About:
This article is published in Computerized Medical Imaging and Graphics.The article was published on 2009-12-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Scale-space segmentation & Mixture model.read more
Citations
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Journal ArticleDOI
Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review
Maria Eugenia Caligiuri,Paolo Perrotta,Antonio Augimeri,Federico Rocca,Aldo Quattrone,Andrea Cherubini +5 more
TL;DR: It is concluded that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives.
Journal ArticleDOI
Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images.
Rita Lopes Simoes,Christoph Mönninghoff,Martha Dlugaj,Christian Weimar,Isabel Wanke,Anne-Marie van Cappellen van Walsum,Anne-Marie van Cappellen van Walsum,Cornelis H. Slump +7 more
TL;DR: This work proposes an automatic lesion segmentation method that uses only three-dimensional fluid-attenuation inversion recovery (FLAIR) images and uses a modified context-sensitive Gaussian mixture model to determine voxel class probabilities, followed by correction of FLAIR artifacts.
Journal ArticleDOI
Image segmentation using spectral clustering of Gaussian mixture models
TL;DR: A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper and the experimental evaluation on the IRIS dataset and the real-world image segmentsation problem demonstrates the effectiveness of the proposed approach.
Journal ArticleDOI
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods
TL;DR: An accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the corpus callosum was developed and can be adapted to perform segmentation on other regions of the brain.
Journal ArticleDOI
Accurate image segmentation using Gaussian mixture model with saliency map
TL;DR: This paper proposes a new model, which incorporates the image content-based spatial information extracted from saliency map into the conventional GMM, and shows that the proposed method outperforms the state-of-the-art methods in terms of accuracy and computational time.
References
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Fuzzy c-means clustering with spatial information for image segmentation.
Keh-Shih Chuang,Hong Long Tzeng,Hong Long Tzeng,Sharon C.-A. Chen,Jay Wu,Jay Wu,Tzong-Jer Chen +6 more
TL;DR: This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.
Book ChapterDOI
Adaptive Segmentation of MRI Data
TL;DR: Adaptive segmentation is described, a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.
Journal ArticleDOI
Image segmentation by clustering
G.B. Coleman,H.C. Andrews +1 more
TL;DR: The technique does not require training prototypes but operates in an "unsupervised" mode and is based on a mathematical-pattern recognition model, which achieves a maximum value that is postulated to represent an intrinsic number of clusters in the data.
Proceedings ArticleDOI
Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets
TL;DR: This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient.
Journal ArticleDOI
Estimating the bias field of MR images
R. Guillemaud,Michael Brady +1 more
TL;DR: The authors show that replacing the class other, which includes all tissue not modeled explicitly by Gaussians with small variance, by a uniform probability density, and amending the expectation-maximization (EM) algorithm appropriately, gives significantly better results.