Semivariogram applied for classification of benign and malignant tissues in mammography

Authors:
Valdeci Ribeiro da Silva, Jr.;Anselmo Cardoso de Paiva;Aristófanes Corrêa Silva;Alexandre Cesar Muniz de Oliveira
Affiliations:
Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Electrical Engineering, Federal University of Maranhão – UFMA, São Luís, MA, Brazil;Department of Computer Science, Federal University of Maranhão – UFMA, São Luís, MA, Brazil
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Year:
2006

Citing 2
Cited 3

Machine learning, neural and statistical classification

Machine learning, neural and statistical classification
Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection

Pattern Recognition Letters

Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Computers in Biology and Medicine
Classification of breast tissues using Getis-Ord statistics and support vector machine

Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
Classification of breast tissues in mammogram images using ripley's K function and support vector machine

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work analyzes the application of the semivariogram function to the characterization of breast tissue as malignant or benign in mammographic images. The method characterization is based on a process that selects, using stepwise technique, from all computed semivariance which best discriminate between the benign and malignant tissues. Then, a multilayer perceptron neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 117 tissues samples, 67 benign and 50 malignant. The result analysis has given a sensitivity of 92.8%, a specificity of 83.3% and an accuracy above 88.0%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue.