STATISTICAL SURFACE FRACTAL ANALYZER OF BREAST CANCER

描述(逐字摘自申请人摘要)：申请者将 开发统计表面分维(S-FD)特征，这将 乳腺肿块的MRI和钼靶摄影鉴别诊断 图像。基于乳房三种功能表征的S-FD特征 将对海量图像数据进行评估：(1)单个MRI上肿块的信号强度 切片；(2)数字化乳腺摄影上的肿块密度；(3)厚度 根据三维核磁共振数据计算的质量。 S-Fd特征是来自于分数阶插值函数模型的统计量 (FIFM)的乳腺肿块图像数据。在以前的研究中，FIFM边界FD(B-FD) 研究表明，在数据受限的情况下，特征可以提供更稳健的区分能力 乳房肿块分析等应用优于其他FD算法。FIFM 表面分形代表良性和非良性之间的多分辨率差异 恶性肿块比FIFM边界更准确、更广泛 分形图，因此可以提供更可靠的歧视信息。 强大的S-FD特征，区分良恶性肿块，将 在开发中的计算机辅助诊断系统中有应用。 建议的商业应用： 新的特征将对诊断医生具有重大价值，他们必须区分 良恶性乳房病变的鉴别。该算法很容易集成到CAD系统中 并且在纹理的各种医疗和工业应用中具有潜在的实用价值 数据受限曲面的分析。

DESCRIPTION (Verbatim from the Applicant's Abstract): The applicants will develop statistical SURFACE fractal dimension (S-fd) features, which will discriminate benign from malignant breast masses on MRI and mammographic images. S-fd features derived from three functional representations of breast mass image data will be evaluated: (1) signal intensity of mass on single MRI slice; (2) mammographic density of mass on digitized mammogram; (3) thickness of mass, computed from 3-dim MRI data. The S-fd features are statistics from Fractal Interpolation Function Models (FIFM) of breast mass image data. In prior research, FIFM BORDER fd (B-fd) features were shown to provide more robust discrimination in data-limited applications such as breast mass analysis than other fd algorithms. FIFM SURFACE fractals represent multiresolution differences between benign and malignant masses more accurately and more extensively than FIFM BORDER fractals, and therefore may provide more reliable discriminatory information. Robust S-fd features, which discriminate benign from malignant masses, will have application in computer-aided-diagnosis systems under development. PROPOSED COMMERCIAL APPLICATION: The new features will have significant value to the diagnostician who must distinguish benign from malignant breast lesions. The algorithm is readily integrated into CAD systems and has potential utility for a variety of medical and industrial applications in texture analysis of data-limited surfaces.