Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach

Thomaz, Carlos Eduardo; Aguiar, Nathan; Oliveira, S.H.A.; Duran, Fábio L.S.; Busatto, Geraldo F.; Gillies, Donald; Rueckert, Daniel

doi:10.1109/sibgrapi.2006.19

Cited by 4 publications

(4 citation statements)

References 17 publications

(8 reference statements)

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“…In the recent years, statistical learning models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) have played an important role for characterizing differences between a reference group of patterns and the population under investigation [1,4,13,24,21,22,23,12]. In general, the basic pipeline to follow in this subject is: (a) Dimensionality reduction; (b) Choose a learning method to compute a separating hypersurface, that is, to solve the classification problem; (c) Reconstruction problem, that means, to consider how good a low dimensional representation might look like.…”

Section: Introductionmentioning

confidence: 99%

Statistical learning approaches for discriminant features selection

et al. 2008

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Section: Introductionmentioning

confidence: 99%

Statistical learning approaches for discriminant features selection

et al. 2008

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“…We use four texture features measured from a gray-level co-occurrence matrix generated from the breast images for classification of the images. A statistical discrimination method (fisherfaces algorithm) [13,14] for feature selection algorithm is also used for extracting discriminative information from extracted feature of medical images to be used as inputs to our classification system.…”

Section: Introductionmentioning

confidence: 99%

An efficient clustering based texture feature extraction for medical image

Mohamed

Abdelsamea

2008

2008 11th International Conference on Computer and Information Technology

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Section: Introductionmentioning

confidence: 99%

Statistical learning approaches for discriminant features selection

Giraldi¹,

Rodrigues²,

Kitani³

et al. 2008

J. Braz. Comp. Soc.

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Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed.

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Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach

Abstract: Abstract

Cited by 4 publications

References 17 publications

Statistical learning approaches for discriminant features selection

Statistical learning approaches for discriminant features selection

An efficient clustering based texture feature extraction for medical image

Statistical learning approaches for discriminant features selection

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