An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fractal Geometry and Computer Graphics
Fractal Geometry and Computer Graphics
Digital Image Processing
Computer Analysis of Visual Textures
Computer Analysis of Visual Textures
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Texture classification using wavelet transform
Pattern Recognition Letters
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Journal of Biomedical Informatics
Machine learning method for knowledge discovery experimented with otoneurological data
Computer Methods and Programs in Biomedicine
WSEAS Transactions on Computers
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Local fractal and multifractal features for volumic texture characterization
Pattern Recognition
Information Sciences: an International Journal
Mammogram retrieval on similar mass lesions
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Computer Methods and Programs in Biomedicine
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Objective: Localized texture analysis of breast tissue on mammograms is an issue of major importance in mass characterization. However, in contrast to other mammographic diagnostic approaches, it has not been investigated in depth, due to its inherent difficulty and fuzziness. This work aims to the establishment of a quantitative approach of mammographic masses texture classification, based on advanced classifier architectures and supported by fractal analysis of the dataset of the extracted textural features. Additionally, a comparison of the information content of the proposed feature set with that of the qualitative characteristics used in clinical practice by expert radiologists is presented. Methods and material: An extensive set of textural feature functions was applied to a set of 130 digitized mammograms, in multiple configurations and scales, constructing compact datasets of textural ''signatures'' for benign and malignant cases of tumors. These quantitative textural datasets were subsequently studied against a set of a thorough and compact list of qualitative texture descriptions of breast mass tissue, normally considered under a typical clinical assessment, in order to investigate the discriminating value and the statistical correlation between the two sets. Fractal analysis was employed to compare the information content and dimensionality of the textural features datasets with the qualitative information provided through medical diagnosis. A wide range of linear and non-linear classification architectures was employed, including linear discriminant analysis (LDA), least-squares minimum distance (LSMD), K-nearest-neighbors (K-nn), radial basis function (RBF) and multi-layer perceptron (MLP) artificial neural network (ANN), as well as support vector machine (SVM) classifiers. The classification process was used as the means to evaluate the inherent quality and informational content of each of the datasets, as well as the objective performance of each of the classifiers themselves in real classification of mammographic breast tumors against verified diagnosis. Results: Textural features extracted at larger scales and sampling box sizes proved to be more content-rich than their equivalents at smaller scales and sizes. Fractal analysis on the dimensionality of the textural datasets verified that reduced subsets of optimal feature combinations can describe the original feature space adequately for classification purposes and at least the same detail and quality as the list of qualitative texture descriptions provided by a human expert. Non-linear classifiers, especially SVMs, have been proven superior to any linear equivalent. Breast mass classification of mammograms, based only on textural features, achieved an optimal score of 83.9%, through SVM classifiers.