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

• Published in: Expert systems with applications Vol. 40; no. 15; pp. 6213 - 6221
• Main Authors: Nascimento, Marcelo Zanchetta do, Martins, Alessandro Santana, Neves, Leandro Alves, Ramos, Rodrigo Pereira, Flores, Edna Lúcia, Carrijo, Gilberto Arantes
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.11.2013; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Biological and medical sciences; CAD; Cancer; Classification; Classifiers; Computer science; control theory; systems; Exact sciences and technology; Gynecology. Andrology. Obstetrics; Mammary gland diseases; Mammography; Mathematical models; Medical sciences; Pattern recognition. Digital image processing. Computational geometry; Polynomial classifier; Polynomials; Surface layer; Texture; Texture analysis; Theoretical computing; Tumors; Wavelet; Wavelet; Polynomial classifier; Texture analysis; Mammography; CAD; Performance evaluation; Image processing; Decision tree; Modeling; Texture; Multiresolution analysis; Cancerology; Classification; Woman; System analysis; Computer aided design; Pattern extraction; Human; Breast cancer; Graph theory; Malignant tumor; Computational complexity; Polynomial time; Image analysis; Wavelet transformation; Receiver operating characteristic curves; Artificial intelligence; Cancer
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2013.04.036

•We propose a system for texture analysis and classification of lesions in mammograms.•We validate the method by quantitative evaluation.•We also made a comparison with other artificial intelligence algorithms.•The method allows classification with average value 0.98 of Az.•The method is effective in 360 digitized mammograms from DDSM database. 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.