Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Digital Image Processing
Applied Numerical Methods for Engineers and Scientists
Applied Numerical Methods for Engineers and Scientists
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
On the computational aspects of Zernike moments
Image and Vision Computing
A new class of Zernike moments for computer vision applications
Information Sciences: an International Journal
Using an image-extended relational database to support content-based image retrieval in a PACS
Computer Methods and Programs in Biomedicine
Comparison between wolfe, boyd, BI-RADS and tabár based mammographic risk assessment
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively.