In this paper, we present a content-based image retrieval system designed to retrieve mammographies from large medical image database. The system is developed based on breast density, according to the four categories defined by the American College of Radiology, and is integrated to the database of the Image Retrieval in Medical Applications (IRMA) project, that provides images with classification ground truth. Two-dimensional principal component analysis is used in breast density texture characterization, in order to effectively represent texture and allow for dimensionality reduction. A support vector machine is used to perform the retrieval process. Average precision rates are in the range from 83% to 97% considering a data set of 5024 images. The results indicate the potential of the system as the first stage of a computer-aided diagnosis framework.
['Júlia Epischina Engrácia de Oliveira', 'Alexei Manso Correa Machado', 'Guillermo Cámara Chávez', 'Ana Paula Brandão Lopes', 'Thomas Martin Deserno', 'Arnaldo de Albuquerque Araújo']
MammoSys: A content-based image retrieval system using breast density patterns