Department of Computer Science and Artificial Intelligence, E.T.S. de Ingenierías Informática y de Telecomunicación, University of Granada, Granada, Spain.
Comput Methods Programs Biomed. 2012 Sep;107(3):497-512. doi: 10.1016/j.cmpb.2011.09.017. Epub 2012 Feb 10.
In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.
为了实现宫颈癌筛查测试的自动化，其中一个最重要且长期存在的挑战是对染色标本中的细胞核进行分割。虽然在高质量采集的单个细胞的核很容易分割，但问题在于在高分辨率扫描完整显微镜载玻片时，需要对具有各种特性的大量核进行分割，并且这些核的采集条件也不同。我们实现了一个能够处理全分辨率图像的系统，并提出了一种新的算法，以便在专家用户的适当控制下对核进行分割。该系统可以自动或交互式地工作，以允许在整个载玻片和图像特征范围内进行分割。它促进了技术和医学专家的数据存储和交互，特别是其基于网络的架构。所提出的算法使用投票方案和先验知识来定位细胞核，然后使用弹性分割算法确定细胞核的精确形状。在进行均值移位和中值滤波去除噪声后，使用 Canny 边缘检测算法提取边缘。受细胞质包围细胞核且细胞核形状大致为椭圆形的观察结果启发，去除与背景相邻的边缘。通过随机霍夫变换对椭圆进行检测，可以找到候选核，然后通过水平集算法对其进行处理。该算法已在包含从两个不同显微镜载玻片获取的 207 张图像的数据库上进行了测试和与其他算法进行了比较，结果令人满意。
