Filipczuk Pawel, Kowal Marek, Obuchowicz Andrzej
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:7368-71. doi: 10.1109/EMBC.2013.6611260.
Digital cytology plays an increasingly important role in breast cancer diagnosis. However, analysis of cytologic images is a very difficult task. Especially, nuclei segmentation is extremely challenging. In our work on fully automated medical diagnosis system we encountered the problem of densely clustered nuclei. We decided to use a segmentation algorithm that is rather rarely found in the literature. Multi-label fast marching was applied and compared to well-known and extensively used seeded watershed algorithm. In both methods, it is critical to determine the appropriate starting points (seeds). The seeds were determined using a combination of adaptive thresholding in grayscale, clustering in color space and conditional erosion. The proposed segmentation procedure was tested for suitability for diagnosis of the cancer. Experiments were conducted on a set of 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The images were classified as either benign or malignant using 84 features extracted from isolated nuclei. Both methods gave very promising results and showed that our method is effective and can be successfully applied for computer-aided diagnosis system.
数字细胞学在乳腺癌诊断中发挥着越来越重要的作用。然而，对细胞学图像进行分析是一项非常困难的任务。特别是，细胞核分割极具挑战性。在我们开发全自动医学诊断系统的工作中，我们遇到了细胞核密集聚集的问题。我们决定使用一种在文献中很少见到的分割算法。应用了多标签快速行进算法，并与广为人知且广泛使用的种子区域生长算法进行了比较。在这两种方法中，确定合适的起始点（种子）至关重要。通过结合灰度自适应阈值处理、颜色空间聚类和条件腐蚀来确定种子。对所提出的分割程序进行了癌症诊断适用性测试。对从波兰绿山城地区医院患者获取的一组450张细针穿刺活检显微图像进行了实验。利用从分离的细胞核中提取的84个特征将图像分类为良性或恶性。两种方法都给出了非常有前景的结果，表明我们的方法是有效的，并且可以成功应用于计算机辅助诊断系统。
