Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
Comput Methods Programs Biomed. 2010 Oct;100(1):1-15. doi: 10.1016/j.cmpb.2010.02.002. Epub 2010 Apr 1.
This paper presents two automated methods for the segmentation of immunohistochemical tissue images that overcome the limitations of the manual approach as well as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.
本文提出了两种自动化的免疫组织化学图像分割方法，克服了手动方法以及现有计算机技术的局限性。第一种独立的方法基于无监督颜色聚类，自动识别标本中的目标癌区并忽略基质；第二种方法基于颜色分离和形态处理，利用癌细胞核膜的自动分割。在真实组织图像上的广泛实验结果表明，与手动分割相比，我们的技术具有更高的准确性；额外的实验表明，与基于监督学习或主动轮廓的流行方法相比，我们的技术在免疫组织化学图像中更有效。所提出的方法可用于任何需要组织和细胞探索的应用，并执行涉及多因素遗传病理的特定蛋白质活性的可靠和标准化测量。
