Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
Med Biol Eng Comput. 2013 Jun;51(6):645-55. doi: 10.1007/s11517-013-1034-9. Epub 2013 Jan 29.
Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %.
神经母细胞瘤是一种起源于神经嵴的恶性肿瘤和儿童癌症。肿瘤中神经母细胞的数量为病理学家提供了重要的预后信息。大量的神经母细胞使得计数过程繁琐且容易出错。我们提出了一种用户交互独立的框架，用于分割细胞区域、分割重叠细胞并计算单个神经母细胞的总数。我们的新分割算法将图像视为由彩色像素的联合空间-强度特征构建的特征空间。它使用均值漂移在特征空间内对像素进行聚类，然后将图像分割成多个块。我们提出了一种新的颜色分析方法来选择与细胞区域相似强度的块。所选的块包含单个和重叠的细胞混合物。因此，我们还提出了一种细胞计数方法来分析细胞的形态，并区分重叠细胞和单个细胞。最终，我们应用分水岭算法来分割重叠的细胞。结果已经由病理学家进行了评估。我们的分割算法与自适应阈值进行了比较。我们的细胞计数算法与两种最先进的算法进行了比较。该系统的整体细胞计数准确率为 87.65%。
