Department of Automation, Xiamen University, Xiamen, China.
PLoS One. 2019 Jan 22;14(1):e0210075. doi: 10.1371/journal.pone.0210075. eCollection 2019.
Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means). Firstly, a contrast enhancement algorithm is implemented in image preprocessing to improve the gridding precision. Secondly, the data-driven means are proposed for cluster center initialization, instead of usual random setting. The third improvement is that the multi features, including intensity features, spatial features, and shape features, are implemented in feature selection to replace the sole pixel intensity feature used in the traditional clustering-based methods to avoid taking noises as spot pixels. Moreover, the principal component analysis is adopted for various feature extraction. Finally, an adaptive adjustment algorithm is proposed based on data mining and learning for further dealing with the missing spots or low contrast spots. Experiments on real and simulation data sets indicate that the proposed improvements made our proposed method obtains higher segmented precision than the traditional K-means and Fuzzy C means clustering methods.
图像分割作为微阵列图像处理的关键步骤，对于同时获得斑点表达至关重要。然而，基于聚类的最新分割算法对噪声很敏感。为了解决这个问题并提高分割精度，本文在快速简单的聚类方法（K-均值和模糊 C 均值）中引入了一些改进。首先，在图像预处理中实现对比度增强算法以提高网格精度。其次，提出了基于数据驱动的聚类中心初始化方法，而不是通常的随机设置。第三个改进是在特征选择中实现了包括强度特征、空间特征和形状特征在内的多种特征，以代替传统基于聚类的方法中仅使用像素强度特征，从而避免将噪声视为斑点像素。此外，还采用主成分分析进行各种特征提取。最后，提出了一种基于数据挖掘和学习的自适应调整算法，以进一步处理缺失斑点或对比度低的斑点。在真实和模拟数据集上的实验表明，所提出的改进使我们的方法比传统的 K-均值和模糊 C 均值聚类方法获得更高的分割精度。
