The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual information retrieval
Information Retrieval
Modern Information Retrieval
Image Databases: Search and Retrieval of Digital Imagery
Image Databases: Search and Retrieval of Digital Imagery
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
An assembled matrix distance metric for 2DPCA-based image recognition
Pattern Recognition Letters
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Challenges of medical image processing
Computer Science - Research and Development
An improved method of breast MRI segmentation with simplified K-means clustered images
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Generic integration of content-based image retrieval in computer-aided diagnosis
Computer Methods and Programs in Biomedicine
Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring
Computers in Biology and Medicine
Breast density classification to reduce false positives in CADe systems
Computer Methods and Programs in Biomedicine
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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.