STATISTICAL SURFACE FRACTAL ANALYZER OF BREAST CANCER

描述（来自申请人摘要的逐字）：申请人将 开发统计表面分形维数（S-fd）功能， MRI和钼靶X线检查鉴别乳腺良恶性肿块 图像.基于乳腺三种功能表征的S-FD特征 将评价肿块图像数据：（1）单个MRI上肿块的信号强度 切片;（2）数字化乳腺X线片上肿块的乳腺X线密度;（3）厚度 根据三维核磁共振成像数据计算的质量。 S-fd要素是来自分形插值函数模型的统计数据 （FIFM）的乳腺肿块图像数据。在先前的研究中，FIFM BORDER fd（B-fd） 特征被证明在数据有限的情况下提供了更强大的区分力。 应用，如乳房肿块分析比其他FD算法。FIFM 表面分形表示良性和良性之间的多分辨率差异， 比FIFM BORDER更准确和更广泛的恶性肿块 分形，因此可以提供更可靠的歧视性信息。 强大的S-fd特征，区分良性和恶性肿块， 在开发中的计算机辅助诊断系统中具有应用。 拟定商业应用： 新的功能将有重大价值的诊断医生谁必须区分 良性和恶性乳腺病变。 该算法易于集成到CAD系统中 并且在质地方面具有用于各种医疗和工业应用的潜在效用 分析数据有限的表面。

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.