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Journal ArticleDOI

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

Semra Icer1
01 Oct 2013-Computer Methods and Programs in Biomedicine (Elsevier)-Vol. 112, Iss: 1, pp 38-46
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.
About: This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2013-10-01. It has received 25 citations till now. The article focuses on the topics: Scale-space segmentation & Segmentation-based object categorization.
Citations
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Book ChapterDOI
01 Jan 2015
TL;DR: A comprehensive survey on FCM and its applications in more than one decade has been carried out to show the efficiency and applicability in a mixture of domains and to encourage new researchers to make use of this simple algorithm.
Abstract: The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various areas have been effectively solved by using FCM and its different variants. But, for efficient use of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains. Also, another intention of this survey is to encourage new researchers to make use of this simple algorithm (which is popularly called soft classification model) in problem solving.

203 citations


Additional excerpts

  • ...The importance of degree of membership [15] in fuzzy clustering is similar to the pixel probability in a mixture 134 J....

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Journal ArticleDOI
TL;DR: In this article, a hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection(SBS).

172 citations


Cites methods from "Automatic segmentation of corpus co..."

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  • ...Different applications of GMM [24–29] motivate us to suggest GMM based feature weighting....

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Journal ArticleDOI
TL;DR: This paper focuses on Fuzzy C-Means based segmentation algorithms and accelerates the execution time of these algorithms using Graphics Process Unit (GPU) capabilities to achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.
Abstract: Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.

68 citations

Journal ArticleDOI
TL;DR: Segmentation of the aspirate smear images using the proposed method helps the analyst in differentiating six groups of cells and in the determination of blasts counting, which will be of great significance for the diagnosis of acute myeloid leukemia.

39 citations

Journal ArticleDOI
TL;DR: This work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images, and uses an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images.
Abstract: Passive millimeter wave (PMMW) imaging has become one of the most effective means to detect the objects concealed under clothing. Due to the limitations of the available hardware and the inherent physical properties of PMMW imaging systems, images often exhibit poor contrast and low signal-to-noise ratios. Thus, it is difficult to achieve ideal results by using a general segmentation algorithm. In this paper, an advanced Gaussian Mixture Model (GMM) algorithm for the segmentation of concealed objects in PMMW images is presented. Our work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images. Our approach is three-fold: First, we remove the noise from the image using both a notch reject filter and a total variation filter. Next, we use an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images. The APIGMM provides an initial number of Gaussian components and start with more appropriate parameter. Bayesian decision is employed to separate the pixels of concealed objects from other areas. At last, the confidence interval (CI) method, alongside local gradient information, is used to extract the concealed objects. The proposed hybrid segmentation approach detects the concealed objects more accurately, even compared to two other state-of-the-art segmentation methods.

33 citations

References
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Book
31 Jul 1981
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

15,662 citations

Book
15 Nov 1996
TL;DR: The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts, opening the door to the tremendous potential of this remarkably versatile statistical tool.
Abstract: The first unified account of the theory, methodology, and applications of the EM algorithm and its extensionsSince its inception in 1977, the Expectation-Maximization (EM) algorithm has been the subject of intense scrutiny, dozens of applications, numerous extensions, and thousands of publications. The algorithm and its extensions are now standard tools applied to incomplete data problems in virtually every field in which statistical methods are used. Until now, however, no single source offered a complete and unified treatment of the subject.The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts. Employing numerous examples, Geoffrey McLachlan and Thriyambakam Krishnan examine applications both in evidently incomplete data situations-where data are missing, distributions are truncated, or observations are censored or grouped-and in a broad variety of situations in which incompleteness is neither natural nor evident. They point out the algorithm's shortcomings and explain how these are addressed in the various extensions.Areas of application discussed include: Regression Medical imaging Categorical data analysis Finite mixture analysis Factor analysis Robust statistical modeling Variance-components estimation Survival analysis Repeated-measures designs For theoreticians, practitioners, and graduate students in statistics as well as researchers in the social and physical sciences, The EM Algorithm and Extensions opens the door to the tremendous potential of this remarkably versatile statistical tool.

5,998 citations

Journal ArticleDOI
TL;DR: Across subjects, the overall density of callosal fibers had no significant correlation withcallosal area and an increased callosal area indicated an increased total number of fibers crossing through, and this was only true for small diameter fibers, whose large majority is believed to interconnect association cortex.

1,340 citations


"Automatic segmentation of corpus co..." refers background in this paper

  • ...[3] F....

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  • ...ognitive, learning, mnemonic and motor information etween the two brain hemispheres [3–5]....

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Journal ArticleDOI
TL;DR: By incorporating local spatial and gray information together, a novel fast and robust FCM framework for image segmentation, i.e., fast generalized fuzzy c-means (FGFCM) clustering algorithms, is proposed and can mitigate the disadvantages of FCM_S and at the same time enhances the clustering performance.

1,021 citations


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Journal ArticleDOI
TL;DR: The complexity of lesions segmentation is described, the automatic MS lesion segmentation methods found, and the validation methods applied are reviewed, to evaluate the state of the art in automated multiple sclerosis lesion segmentsation.

340 citations


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  • ...[31] D....

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