Center for Machine Perception, Czech Technical University, Department of Cybernetics, Faculty of Electrical Engineering, Prague, Czech Republic.
Comput Biol Med. 2011 Oct;41(10):960-70. doi: 10.1016/j.compbiomed.2011.07.010. Epub 2011 Sep 3.
Colposcopy is a well-established method to detect and diagnose intraepithelial lesions and uterine cervical cancer in early stages. During the exam color and texture changes are induced by the application of a contrast agent (e.g.3-5% acetic acid solution or iodine). Our aim is to densely quantify the change in the acetowhite decay level for a sequence of images captured during a colposcopy exam to help the physician in his diagnosis providing new tools that overcome subjectivity and improve reproducibility. As the change in acetowhite decay level must be calculated from the same tissue point in all images, we present an elastic image registration scheme able to compensate patient, camera and tissue movement robustly in cervical images. The image registration is based on a novel multi-feature entropy similarity criterion. Temporal features are then extracted using the color properties of the aligned image sequence and a dual compartment tissue model of the cervix. An example of the use of the temporal features for pixel-wise classification is presented and the results are compared against ground truth histopathological annotations.
阴道镜检查是一种成熟的方法，用于检测和诊断上皮内病变和子宫颈癌的早期阶段。在检查过程中，通过应用对比剂（例如 3-5%的醋酸溶液或碘溶液）来诱导颜色和纹理的变化。我们的目的是对阴道镜检查过程中拍摄的一系列图像中的醋酸白褪色水平的变化进行密集量化，以帮助医生进行诊断，提供新的工具，克服主观性并提高可重复性。由于醋酸白褪色水平的变化必须从所有图像中的同一组织点计算，因此我们提出了一种弹性图像配准方案，能够在宫颈图像中稳健地补偿患者、相机和组织的运动。图像配准基于一种新的多特征熵相似性准则。然后使用对齐图像序列的颜色特性和宫颈的双室组织模型提取时间特征。展示了使用时间特征进行逐像素分类的示例，并将结果与地面真实组织病理学注释进行了比较。
