Improvement in ann performance by the selection of the best texture features from breast masses in mammography images

Tipo
Artigo de evento
Data de publicação
2007
Periódico
IFMBE Proceedings
Citações (Scopus)
1
Autores
Ribeiro P.B.
Schiabel H.
Patrocinio A.C.
Orientador
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
© International Federation for Medical and Biological Engineering 2007.Artificial Neural Network (ANN) are frequentely used as classifiers in Computer Aided-Diagnosis (CAD) schemes. The classifier performance depends on the features extraction to represent each class. In this work, 14 features were extracted from mammographic images by using Haralick texture technique. Regions of interest (ROI) containing mass and normal regions were used. By using Gaussian distribution of features into classes, identifing the distance between classes for each feature was possible select the bests. The implemented Multi Layer Perceptron (MLP) and Self-Organizing Map (SOM) classifiers, tested with the 14 selected Haralick texture features, yields a rate of 86.67% and 81.66% of efficacy, respectively, in classifying the cases of suspicious nodules. However, after performing the Gaussian distribution analysis, the 6 best texture features were selected. Thus, tests with the same MLP and SOM classifiers using this new restrict features set have resulted in na efficacy of 91.50% for MLP and 85.83% for SOM in the cases of suspicious nodules, which means an improvement of 4-5% in the classifier performance.
Descrição
Palavras-chave
Assuntos Scopus
Breast Cancer , Classifier performance , Computer Aided Diagnosis(CAD) , Features extraction , Haralick texture features , Mammographic images , Multi layer perceptron , Regions of interest
Citação
DOI (Texto completo)