Gaussian Mixture Model : Application to Medical Image Classification

A. Vignesh Kumar

ISBN 10: 6139987954 / ISBN 13: 9786139987955

Published by LAP LAMBERT Academic Publishing, 2018

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.

Seller Inventory # 9786139987955

Bibliographic Details

• Title: Gaussian Mixture Model : Application to Medical Image Classification
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 6139987954
• ISBN 13: 9786139987955
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 102 grams

About this title

Synopsis

Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.