Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
Comput Med Imaging Graph. 2010 Sep;34(6):453-61. doi: 10.1016/j.compmedimag.2009.12.008. Epub 2010 Jan 8.
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours' limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations.
本文针对癌症组织图像中的核分割问题进行了研究，该问题对于特定蛋白质活性的定量分析以及癌症的诊断和治疗至关重要。我们提出了一种完全自动化的基于形态学的技术，能够在具有异质性染色和多个组织层的图像中进行精确的核分割，并将其与基于成熟分割方法的另一种半自动方法进行了比较，即活动轮廓。我们讨论了活动轮廓在免疫组织化学图像分割中的局限性，并通过广泛的实验证明和证明了我们的全自动方法相对于各种活动轮廓实现的更高的准确性。
