FRACTAL FEATURES FOR DIAGNOSING MAMMOGRAPHIC MASSES

我们建议开发用于量化病变的统计分形特征 乳房X光片的边缘粗糙度及利用这些特征来区分 恶性和良性乳腺病变。损伤的客观测量 粗糙度在乳腺癌的诊断和分期中很重要。 在这种新的方法中，我们生成了一个由分形模型组成的空间，并且 从统计学的角度评价病变的分维 模型空间的一部分。 以强度作为第三维，我们生成了一个三维的 乳房X光照片的图像，并选择在 损伤。对于每个选定的强度级别，我们构造一个二进制 对图像进行阈值处理，跟踪病变边缘。分析受到限制 到位于高梯度部分的阈值边界段 并且可以高精度地确定。 分析每个选定的边界段以识别自仿射 使用多重分形内插法建模的子段 具有已知FD值的函数。因此，我们构建了一个大样本 从高精度边界计算的FD值的空间 在一系列阈值水平上出现的踪迹。的统计数字。 FD样本空间是用来区分恶性的特征 和良性病变。 建议的商业应用 我们的产品将对诊断医生具有重大价值， 区分乳房良性病变和恶性病变。该算法是 易于集成到计算机辅助诊断系统和 显示和处理乳房X光图像的数字乳房X光系统 给专家诊断师。

We propose developing statistical fractal features which quantify lesion border roughness on mammograms and using these features to distinguish malignant and benign breast lesions. Objective measures of lesion roughness are important in the diagnosis and staging of breast cancer. In this novel approach, we generate a space of fractal models, and evaluate the fractal dimension (fd) of the lesion from the statistics of the model space. Using intensity as the third dimension, we generate a 3-dimensional image of the mammogram and select a set of intensity levels found in the lesion. For each selected intensity level, we construct a binary thresholded image and trace the lesion border. Analysis is restricted to segments of thresholded borders which are in high-gradient portions of the 3-dimensional image and can be determined with high precision. Each selected boundary segments is analyzed to identify self-affine subsegments which are modeled using multiple fractal interpolation functions having known fd values. Thus we construct a large sample space of fd values which are computed from high-precision boundary traces occurring on a range of threshold levels. The statistics of the fd sample space are the features used to distinguish between malignant and benign lesions. PROPOSED COMMERCIAL APPLICATION Our product will have significant value to the diagnostician who must distinguish malignant from benign breast lesions. The algorithm is readily integrated into both computer-aided diagnosis systems and digital mammogram systems which display and process mammographic images for the expert diagnostician.