Abstract
Content-based image retrieval (CBIR) has recently emerged as a promising method to assist radiologists in diagnosing mammographic masses by displaying pathologically similar cases. In this paper, a CBIR system using curvelet transform and kernel principal component analysis (KPCA) is proposed. Thanks to its improved direction and edge representation abilities, curvelet transform first provides desirable mammographic features. Once the region of interest (ROI) is curvelet transformed, the KPCA is then applied and the first components are used as descriptors. Bearing in mind that neighbor points are the most important but faraway points may contain useful information in mammogram retrieval, we propose a new mixed kernel that overcomes the shortcoming of Gaussian kernels and emphasis neighbor points without neglecting faraway ones. The proposed mixed kernel is a mixture of two gaussian kernels with high and low sigma values. Experiments performed on a large dataset of mammograms showed the superiority of the proposed kernel over single gaussian kernels.