Machine learning methods for classifying mammographic regions using the wavelet transform and radiomic texture features

Jaider Stiven Rincón, Andrés E. Castro-Ospina, Fabián R. Narváez, Gloria M. Díaz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Automatic detection and classification of lesions in mammography remains one of the most important and challenging problems in the development of computer-aided diagnosis systems. Several machine learning approaches have been proposed for supporting the detection and classification of mammographic findings, and are used as computational tools during different diagnosis process by the radiologists. However, the effectiveness of these approaches depends on the accuracy of the feature representation and classification techniques. In this paper, a radiomic strategy based on texture features is explored for identifying abnormalities in mammographies. For doing that, a complete study of five feature extraction approaches, ten selection methods, and five classification models was carried out for identifying findings contained in regions of interest extracted from mammography. The proposed strategy starts with a region extraction process. Some square regions of interest (ROI) were manually extracted from the Mammographic Image Analysis Society (miniMIAS) database. Then, each ROI was decomposed into different resolution levels by using a Wavelet transform approach, and a set of radiomic features based on texture information was computed. Finally, feature selection algorithms and machine learning models were applied to decide whether the ROI undergoing analysis contains or not a mammographic abnormality. The obtained results showed that radiomic texture descriptors extracted from wavelet detail coefficients improved the performance obtained by radiomic features extracted from the original image.

Original languageEnglish
Title of host publicationTechnology Trends - 4th International Conference, CITT 2018, Revised Selected Papers
EditorsMiguel Botto-Tobar, Mayra D’Armas, Miguel Zúñiga Sánchez, Miguel Zúñiga-Prieto, Guillermo Pizarro
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030055318
StatePublished - 1 Jan 2019
Event4th International Conference on Technology Trends, CITT 2018 - EC, Babahoyo, Ecuador
Duration: 29 Aug 201831 Aug 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference4th International Conference on Technology Trends, CITT 2018


  • Breast cancer
  • Machine learning methods
  • Radiomics
  • ROI classification


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