Automatic microarray image segmentation with clustering-based algorithms

• Published in: PloS one 2019-01, Vol.14 (1), p.e0210075-e0210075
• Main Authors: Shao, Guifang, Li, Dongyao, Zhang, Junfa, Yang, Jianbo, Shangguan, Yali
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; Analysis; Bioinformatics; Biology and Life Sciences; Cluster Analysis; Clustering; Computer and Information Sciences; Computer science; Computer simulation; Data mining; Data processing; Engineering; Engineering and Technology; Feature extraction; Fuzzy Logic; Gene expression; Genetic algorithms; Genomes; Humans; Image contrast; Image enhancement; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Machine learning; Medical imaging; Medical research; Morphology; Oligonucleotide Array Sequence Analysis - methods; Parameter estimation; Pattern Recognition, Automated - methods; Physical Sciences; Pixels; Principal components analysis; Regularization methods; Reproducibility of Results; Research and Analysis Methods; Segmentation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0210075

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.