FRACTAL-DIMENSION FEATURES FOR MRI BREAST-MASS ANALYSIS

这个项目的目标是：(1)制作一个原型，它将演示通过分析确定的MRI特征、专家发现和患者数据的组合如何帮助诊断医生改进对已知的局灶性乳腺肿块患者的管理，以及(2)设计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维(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特征是根据质量边界段的分形内插函数模型(FIFM)空间的统计量来计算的。使用来自多个阈值级别的边界段。统计方法提供了其他FD估计器所没有的稳健性。在第一阶段可行性研究中，FIFM和专家-观察员特征的结合改善了仅有专家-观察员特征的辨别力。拟议的商业应用：一个有助于诊断医生改进对已知乳腺肿块患者的管理的系统将对有兴趣开发和推广用于乳腺癌诊断的MRI的MRI中心和开发商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.