Information-Theoretic Based CAD in Mammography

基于信息理论的乳腺 X 线摄影 CAD

• 批准号: 7336275
• 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 2009-08-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7336275
• 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）：

项目成果

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.

• DOI: 10.1118/1.3132304
• 发表时间: 2009-07
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: M. Mazurowski;J. Zurada;G. Tourassi

Probabilistic framework for reliability analysis of information-theoretic CAD systems in mammography.

乳腺 X 线摄影中信息理论 CAD 系统可靠性分析的概率框架。

• DOI: 10.1109/iembs.2006.260500
• 发表时间: 2006
• 期刊: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
• 作者: Habas,PiotrA;Zurada,JacekM;Elmaghraby,AdelS;Tourassi,GeorgiaD
• 通讯作者: Tourassi,GeorgiaD

Reliability analysis framework for computer-assisted medical decision systems.

计算机辅助医疗决策系统的可靠性分析框架。

• DOI: 10.1118/1.2432409
• 发表时间: 2007
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Habas,PiotrA;Zurada,JacekM;Elmaghraby,AdelS;Tourassi,GeorgiaD
• 通讯作者: Tourassi,GeorgiaD

Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach.

3D 重建断层合成体积中的自动乳腺肿块检测：一种无特征的方法。

• DOI: 10.1118/1.2953562
• 发表时间: 2008
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Singh,Swatee;Tourassi,GeorgiaD;Baker,JayA;Samei,Ehsan;Lo,JosephY
• 通讯作者: Lo,JosephY

The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.

• DOI: 10.1016/j.neunet.2011.07.002
• 发表时间: 2012-01
• 期刊: Neural networks : the official journal of the International Neural Network Society
• 作者: Malof JM;Mazurowski MA;Tourassi GD
• 通讯作者: Tourassi GD