FRACTAL-DIMENSION FEATURES FOR MRI BREAST-MASS ANALYSIS

用于 MRI 乳腺肿块分析的分形维特征

• 批准号: 6173487
• 负责人: ALAN I. PENN
• 金额: $ 37.94万
• 依托单位: ALAN PENN AND ASSOCIATES, INC.
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-11-01 至 2002-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6173487
• 关键词: bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; breast neoplasms; computer assisted diagnosis; computer system design /evaluation; diagnosis design /evaluation; female; human data; magnetic resonance imaging; mathematical model; mathematics; model design /development; statistics /biometry

The objectives of this project are: (1) to produce a prototype which will demonstrate how a combination of analytically-determined MRI features, expert findings and patient data can aid the diagnostician in improving the management of patients with known focal breast masses, and (2) design a clinical evaluation of a commercial system. The prototype will generate likelihood estimates of malignancy using expert-observer readings, pre-test risk factors and computer-generated fractal dimension (fd) features as input to a non-linear discriminator. The fd feature is computed as statistics from a space of fractal interpolation function models (FIFM) of boundary segments of the mass. Boundary segments from multiple threshold levels are used. The statistical approach provides a robustness which is not found in other fd estimators. In the Phase I feasibility study, the combination of FIFM and expert-observer features generated improved discrimination over expert-observer features alone. PROPOSED COMMERCIAL APPLICATION: A system which is an aid to the diagnostician in improving the management of patients with known breast masses will have a significant market value to MRI centers and developers who have an interest in developing and promoting MRI for breast cancer diagnosis.

本项目的目标是：（1）生产一个原型，该原型将证明分析确定的MRI特征、专家发现和患者数据的组合如何帮助诊断医生改善已知局灶性乳腺肿块患者的管理，以及（2）设计一个商业系统的临床评价。该原型将使用专家观察员读数、预测试风险因素和计算机生成的分形维数（fd）特征作为非线性神经网络的输入来生成恶性肿瘤的可能性估计。 fd特征被计算为来自质量的边界段的分形插值函数模型（FIFM）的空间的统计。 使用来自多个阈值水平的边界段。 统计方法提供了一个鲁棒性，这是没有发现在其他FD估计。 在第一阶段的可行性研究中，FIFM和专家观察员功能的组合产生了比单独的专家观察员功能更好的辨别力。拟定商业应用：一个系统，这是一个帮助诊断医生在改善与已知的乳腺肿块的患者的管理将有显着的市场价值，MRI中心和开发商谁有兴趣在开发和推广MRI乳腺癌诊断。