Information-Theoretic Based CAD in Mammography

基于信息理论的乳腺 X 线摄影 CAD

• 批准号: 7162911
• 负责人: Georgia Tourassi
• 金额: $ 23.07万
• 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/7162911
• 关键词: Algorithms; Archives; Artificial Intelligence; Classification; Clinical; Computer Assisted; Computer Interface; Cues; Databases; Deposition; Detection; Diagnosis; Diagnostic; Drug Formulations; Early Diagnosis; Entropy; Image; Information Theory; Knowledge; Knowledge Base (Computer); Libraries; Malignant - descriptor; Mammography; Mass in breast; Measures; Metric; Physicians; Purpose; Radiology Specialty; Scheme; Screening procedure; Specificity; Standards of Weights and Measures; Structure; System; Techniques; Uncertainty; base; concept; digital imaging; improved; knowledge base; novel; radiologist; research clinical testing; statistics; trend

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）： 本研究的目的是开发和评估一种新的计算机辅助决策（CAD）方案，以提高乳腺肿块的临床检测筛查乳腺X线照片。CAD方案结合信息理论的相似性度量与基于知识的决策算法。它将帮助放射科医生仔细检查乳房X光片，提供基于证据的决策支持。给定查询乳房X光检查区域，CAD系统将询问存档乳房X光检查数据库，检查类似病例，并分配有关潜在恶性肿块存在的可能性度量。 该研究提出了制定信息理论的指标来量化两个乳房摄影区域的相似性。相似性度量基于Sharmon熵;图像中包含的复杂性（或信息）的度量。从理论上讲，如果两个乳房X线摄影区域描绘了相似的结构，它们应该包含彼此的诊断信息。相关诊断信息的量可以通过基于熵的相似性度量来测量，该相似性度量直接从图像计算而不需要分割或特征提取。使用的相似性度量和图像数据库的乳腺X线摄影的情况下，与已知的真相，知识总线的CAD计划将被实施用于检测群众在筛查乳腺X线摄影。初步研究表明，标准互信息（MI）是一个有效的相似性度量的任务。 本研究的具体目的是：（1）充分利用测量两个乳房X线摄影区域相似内容的信息理论指标，（2）优化其在循证决策算法中的贡献，以早期检测潜在的恶性肿块，（3）对CAD系统进行初步临床评价。 由于数字图像库是放射学的一个即将到来的趋势，因此所提出的CAD系统将利用具有已建立的基础事实的连续沉积的乳房X线照片。该系统旨在减少与筛查乳腺X线摄影相关的判读错误和/或提示CAD方案产生的假阳性。总体而言，该研究旨在提高灵敏度，同时保持或提高筛查乳腺X线摄影对肿块的特异性。