FRACTAL DIMENSION FEATURES FOR MRI BREAST MASS ANALYSIS

用于 MRI 乳腺肿块分析的分形维度特征

• 批准号: 2874232
• 负责人: ALAN I. PENN
• 金额: $ 3.08万
• 依托单位: ALAN PENN AND ASSOCIATES, INC.
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-25 至 1999-03-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2874232
• 关键词: 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; method development; model design /development

We propose developing a robust algorithm to evaluate the fractal dimension (fd) of borders of Magnetic Resonance (MR) images of breast masses which contain a small number of pixels. The fd algorithm will be evaluated in upcoming MR breast clinical trials and will be marketed to developers of computer-aided diagnosis systems for MR breast imaging. Contrast-enhanced MR is a promising tool for detecting and diagnosing masses in dense, radio-opaque and scarred breasts. Since border roughness is correlated to breast cancer and fd is a measure of roughness, the proposed algorithm will generate an objective indicator of malignancy. Current algorithms for estimating fd which use box counting or fractional Brownian motion are non-robust when applied to images with limited pixel data. The proposed algorithm generates a family of fractal interpolation function models and derives robust fd estimates from she statistics of the models. The proposed algorithm computes the following features: (a) an estimate of fd of the mass border, (b) a measure of the reliability of the estimate, (c) a measure of the extent of self-affinity of the border, and (d) a measure of the stability of the estimate over a range of threshold levels. PROPOSED COMMERCIAL APPLICATION: A computer-aided diagnosis system which reliably discriminates benign from malignant masses will have a significant market value to MR! centers and developers who have an interest in developing and promoting MR! as an adjunctive method of screening for breast cancer. Since our product enhances the performance of such a system, there is a large commercial potential and a readily identified pool of prospective strategic partners.

我们建议开发一个强大的算法来评估分形 乳房磁共振（MR）图像边界的尺寸（fd） 包含少量像素的质量。FD算法将是 在即将进行的MR乳腺临床试验中进行评估，并将销售给 MR乳腺成像计算机辅助诊断系统的开发人员。 对比增强MR是一种很有前途的检测和诊断工具 致密、不透射线和疤痕乳房中的肿块。由于边境 粗糙度与乳腺癌相关，FD是 粗糙度，所提出的算法将产生一个客观的指标 恶性肿瘤当前使用box的fd估计算法 计数或分数布朗运动在应用于 有限像素数据的图像。所提出的算法生成一个 族分形插值函数模型，并导出了鲁棒FD 根据模型的统计数据进行估计。该算法 计算以下特征：（a）质量的fd的估计 边界，（B）估计的可靠性的度量，（c）度量 （一）以人为本;（二）以人为本。 估计在阈值水平范围内的稳定性。 拟定商业应用： 一种可靠地鉴别良性肿瘤的计算机辅助诊断系统 从恶性肿块将有重大的市场价值的MR！ 有兴趣开发和推广的中心和开发商 先生！作为一种筛查乳腺癌的有效方法。由于我们 产品增强了这样一个系统的性能，有一个大的 商业潜力和一个容易确定的潜在池 战略伙伴

项目成果

Discrimination of MR images of breast masses with fractal-interpolation function models.

用分形插值函数模型区分乳腺肿块的 MR 图像。

• DOI: 10.1016/s1076-6332(99)80401-2
• 发表时间: 1999
• 期刊: Academic radiology.
• 作者: Penn,AI;Bolinger,L;Schnall,MD;Loew,MH
• 通讯作者: Loew,MH