Suckling J, Dance D R, Moskovic E, Lewis D J, Blacker S G
Joint Department of Physics, Institute of Cancer Research, London, United Kingdom.
Med Phys. 1995 Feb;22(2):145-52. doi: 10.1118/1.597464.
A possible first stage in the analysis of the mammographic scene is its segmentation into four major components: background (the nonbreast area), pectoral muscle, fibroglandular region (parenchyma), and adipose region. An algorithm has been developed for this task. It is based on the classification of a feature vector constructed from statistical measures of texture calculated at two window sizes. Separate self-organizing neural networks are trained on sample data taken from each of the four regions. The feature vectors from the entire mammogram are then classified with the trained networks linked via a decision logic. To overcome the variability of texture between mammograms the algorithm uses data from a mammogram to classify itself in a staged approach consisting of several binary decisions. The training regions for each successive stage are determined from geometric information produced by the previous stages. The dataset in the study consisted of thirty (fifteen pairs) digitized normal mammograms of variable radiographic appearance. As a measure of performance, the outlines of the parenchyma were compared to those drawn by a radiologist experienced in reading mammograms. Comparison of the areas and perimeters generated by the human and computer observers gives a relationship with correlation coefficients of 0.74 and 0.59 for each measure, respectively. The overlapping areas of the parenchymas segmented by the observers normalized by the combined area was also calculated for each case. The mean and standard deviation of this measure was 0.69 +/- 0.12.
乳腺X线图像分析的第一个可能阶段是将其分割为四个主要部分：背景（非乳腺区域）、胸肌、纤维腺体区域（实质）和脂肪区域。针对此任务开发了一种算法。它基于从两种窗口大小计算出的纹理统计量构建的特征向量的分类。在从四个区域中的每个区域获取的样本数据上训练单独的自组织神经网络。然后，通过决策逻辑链接经过训练的网络，对整个乳腺X线图像的特征向量进行分类。为了克服不同乳腺X线图像之间纹理的变异性，该算法使用乳腺X线图像的数据，通过由几个二元决策组成的分阶段方法对其自身进行分类。每个连续阶段的训练区域由前一阶段产生的几何信息确定。该研究中的数据集由30幅（15对）具有不同放射学表现的数字化正常乳腺X线图像组成。作为性能指标，将实质的轮廓与由一位有乳腺X线图像阅读经验的放射科医生绘制的轮廓进行比较。对人类观察者和计算机观察者生成的面积和周长进行比较，每种测量的相关系数分别为0.74和0.59。还针对每个病例计算了观察者分割的实质的重叠面积，并以组合面积进行归一化。该测量的平均值和标准差为0.69±0.12。
