Information-Theoretic Based CAD in Mammography

基于信息理论的乳腺 X 线摄影 CAD

• 批准号: 6873106
• 负责人: GEORGIA D TOURASSI
• 金额: $ 23.91万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-21 至 2008-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6873106
• 关键词: bioimaging /biomedical imaging; bioinformatics; breast neoplasms; computer assisted medical decision making; diagnosis design /evaluation; digital imaging; human data; library; mammography; mathematical model; model design /development; neoplasm /cancer diagnosis

DESCRIPTION (provided by applicant): The purpose of the study is to develop and evaluate a novel computer-assisted decision (CAD) scheme for improving the clinical detection of breast masses in screening mammograms. The CAD scheme combines information-theoretic similarity metrics with knowledge-based decision algorithms. It will help radiologists scrutinize mammograms providing evidence-based decision support. Given a query mammographic region, the CAD system will interrogate a database of archived mammograms, examine similar eases, and assign a likelihood measure regarding the presence of a potentially malignant mass. The study proposes the formulation of information-theoretic metrics to quantify the similarity of two mammographic regions. The similarity metrics are based on Sharmon's entropy; a measure of complexity (or information) contained in an image. Theoretically, if two mammographic regions depict similar structures, they should contain diagnostic information for each other. The amount of relevant diagnostic information can be measured by entropy-based similarity metrics that are computed directly from the images without requiring segmentation or feature extraction. Using the similarity metrics and an image databank of mammographic cases with known truth, a knowledge-bussed CAD scheme will be implemented for the detection of masses in screening mammograms. Preliminary studies have established that standard mutual information (MI) is an effective similarity metric for the task. The specific aims of the study are: (1) To fully exploit information-theoretic metrics that measure the similar content of two mammographic regions, (2) To optimize their contributions in an evidence-based decision algorithm for the early detection of potentially malignant masses, and (3) To perform preliminary clinical evaluation of the CAD system. As digital image libraries are an upcoming trend in radiology, the proposed CAD system will take advantage of continuously deposited mammograms with established ground truth. The system aims to reduce the interpretation error associated with screening mammograms and/or the false positives generated by cuing CAD schemes, Overall, the study aims to improve the sensitivity while maintaining or improving the specificity of screening mammography for masses.

描述（由申请人提供）：