Correlative Feature Analysis for Multimodality Breast CAD
Annual summary rept. 1 Sep 2006-31 Aug 2009
CHICAGO UNIV IL
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The purpose of the study is to develop correlative feature analysis methods for integrating image information from multimodality breast images, taking advantage of the information from different views andor different modalities, and thus improving the sensitivity and specificity of breast cancer diagnosis. Identifying the corresponding image pair of a lesion is an essential step for this purpose. During the past three years, we have built a multi-modality database which includes FFDM, breast US and DCE-MR images. We also developed computerized correlative feature analysis methods including automatic lesion segmentation, feature extraction and selection, feature correlation analysis and image pair classification in differentiating corresponding and non corresponding lesions across different mammographic views andor different imaging modalities. The results show that the proposed correlative feature analysis is effective and robust for the discrimination between corresponding and non-corresponding lesion pairs.
- Medicine and Medical Research