Segmentation of Folds in Tissue Section Images
Segmentation of Folds in Tissue Section Images
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组织切片图像中褶皱的分割
DOI：
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发表时间：
2007
期刊：
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通讯作者：
O. Yli
中科院分区：
文献类型：
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作者：
S. Palokangas;J. Selinummi;O. Yli
提出了一种用于组织切片图像中褶皱识别的自动图像分析方法。组织折叠是组织学图像中常见的伪影。当组织在显微镜载玻片上折叠两次或两次以上时，就会形成折叠伪影。在对细胞核进行自动分析时，这些伪影的存在容易导致算法产生错误的输出。因此，他们的识别是必不可少的，以获得可靠的分析。提出的多阶段算法包括三个阶段。首先，将截面图像转换为HSI色彩空间，并对其饱和度和强度分量进行处理，以增强对物象点的识别能力；接下来，使用k均值聚类进行分割，并从其他聚类中提取包含折叠像素的聚类。最后，根据被分割对象的大小和分量值，对不可避免的分割错误进行校正，这些错误主要是由带有褶皱的相似特征的核引起的。在不同的组织切片图像上进行了测试，并与人工获得的结果进行了比较，结果令人满意。
An automated image analysis method for identifying folds in tissue section images is presented. Tissue folding is a common artifact in histological images. Folding artifacts form when tissue folds over twice or more when placing it on the microscope slide. As analyzing cell nuclei automatically, the existence of these artifacts causes algorithms easily to give false output. Thus, their identification is essential in order to obtain reliable analysis. The proposed multistage algorithm consists of three phases. First, the section image is converted to HSI color-space and the saturation and intensity components are processed in order to enhance the discrimination of the objective pixels. Next, segmentation is performed using k- means clustering and the cluster containing fold pixels is extracted from the others. Finally, unavoidable segmentation errors caused mostly by the nuclei of similar characteristics with folds are corrected based on the size and component values of the faulty segmented objects. The method is tested on different tissue section images and the results are compared with manually obtained ones with promising results.