Content-based retrieval of mammograms using visual features related to breast density patterns.
This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick's texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.
Kinoshita
Sérgio Koodi
SK
Image Science and Medical Physics Center, Internal Medicine Department, Faculty of Medicine of Ribeirao Preto, University of Sao Paulo, avenida dos Bandeirantes, 3900, 14048-900, Ribeirão Preto, São Paulo, Brazil.
de Azevedo-Marques
Paulo Mazzoncini
PM
Pereira
Roberto Rodrigues
RR
Rodrigues
Jośe Antônio Heisinger
JA
Rangayyan
Rangaraj Mandayam
RM
D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000033:anatomy & histology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D003628:Database Management Systems; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data; D008327:Mammography / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D011857:Radiographic Image Interpretation, Computer-Assisted