FRACTAL-DIMENSION FEATURES FOR MRI BREAST-MASS ANALYSIS

该项目的目标是：(1)制作一个原型，该原型将展示如何将分析确定的MRI特征、专家发现和患者数据结合起来，帮助诊断医生改善对已知灶性乳房肿块患者的管理；(2)设计一个商业系统的临床评估。该原型将使用专家-观察者读数、测试前风险因素和计算机生成的分形维数（fd）特征作为非线性鉴别器的输入，生成恶性肿瘤的可能性估计。fd特征是通过质量边界段的分形插值函数模型（FIFM）空间的统计量来计算的。使用来自多个阈值水平的边界段。统计方法提供了其他fd估计器所没有的鲁棒性。在第一阶段的可行性研究中，FIFM和专家-观察者特征的结合产生了比单独的专家-观察者特征更好的识别。建议的商业应用：一个系统可以帮助诊断医生改善对已知乳房肿块患者的管理，对于有兴趣开发和推广MRI用于乳腺癌诊断的MRI中心和开发商来说，将具有重要的市场价值。

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.