Kupinski M A, Giger M L
Department of Radiology, The University of Chicago, IL 60637, USA.
IEEE Trans Med Imaging. 1998 Aug;17(4):510-7. doi: 10.1109/42.730396.
Segmenting lesions is a vital step in many computerized mass-detection schemes for digital (or digitized) mammograms. We have developed two novel lesion segmentation techniques-one based on a single feature called the radial gradient index (RGI) and one based on simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In both methods a series of image partitions is created using gray-level information as well as prior knowledge of the shape of typical mass lesions. With the former method the partition that maximizes the RGI is selected. In the latter method, probability distributions for gray-levels inside and outside the partitions are estimated, and subsequently used to determine the probability that the image occurred for each given partition. The partition that maximizes this probability is selected as the final lesion partition (contour). We tested these methods against a conventional region growing algorithm using a database of biopsy-proven, malignant lesions and found that the new lesion segmentation algorithms more closely match radiologists' outlines of these lesions. At an overlap threshold of 0.30, gray level region growing correctly delineates 62% of the lesions in our database while the RGI and probabilistic segmentation algorithms correctly segment 92% and 96% of the lesions, respectively.
在许多用于数字（或数字化）乳腺X线摄影的计算机化肿块检测方案中，分割病变是关键步骤。我们开发了两种新颖的病变分割技术——一种基于名为径向梯度指数（RGI）的单一特征，另一种基于简单概率模型，用于从周围背景中分割出肿块病变或其他类似的结节状结构。在这两种方法中，利用灰度信息以及典型肿块病变形状的先验知识创建一系列图像分区。对于前一种方法，选择使RGI最大化的分区。对于后一种方法，估计分区内外灰度的概率分布，随后用于确定每个给定分区出现该图像的概率。选择使该概率最大化的分区作为最终的病变分区（轮廓）。我们使用经活检证实的恶性病变数据库，将这些方法与传统区域生长算法进行对比测试，发现新的病变分割算法与放射科医生对这些病变的轮廓划分更为接近。在重叠阈值为0.30时，灰度区域生长正确描绘出我们数据库中62%的病变，而RGI和概率分割算法分别正确分割出92%和96%的病变。
