Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Authors:
Marcelo Zanchetta Do Nascimento;Alessandro Santana Martins;Leandro Alves Neves;Rodrigo Pereira Ramos;Edna LúCia Flores;Gilberto Arantes Carrijo
Affiliations:
-;-;-;-;-;-
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 12
Cited 0

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Multiresolution mammogram analysis in multilevel decomposition

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Segmentation of regions of interest in mammograms in a topographic approach

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Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

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Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

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SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation

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Abstract

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98+/-0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.