MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU

An Automated cDNA Microarray Image Analysis for the Determination of Gene Expression Ratios

This paper proposes a fully automated technique for cDNA microarray image analysis. Initially, an effective preprocessing stage combined with gridding is built to get the individual spot regions of images. Current work begins with the proposal of a new rule to get the foreground (spot) and background regions in the spot blocks, which uses TV-L1 image denoising, spot block binarization, and finds the most accurate spot label by measuring the centroid differences of labelled regions in the block with that of the spot block centroid. The credibility of the segmentation rule on real images is evaluated by metrics: mean absolute error (MAE) and coefficient of variation (CV) and on synthetic images by metrics: probability of error (PE) and discrepancy distance (DD). The performance values on real and synthetic datasets reveal better results than the competitive methods. After the segmentation, prior to the spot intensity extraction, background intensity correction and flagging of noisy spots are executed. Using the lowess method, intensities are normalized, and gene expression ratios are determined. To comprehend the linearities of red and green intensities and to discern up and down-regulated genes (abnormal), fold-change factor, scatter and box plots are also used to represent the gene expression levels.

A Bio-inspired Algorithm based Multi-class Classification Scheme for Microarray Gene Data

Microarray gene data is widely known for its high dimensionality and volume. The utilization of microarray gene data is increasing now-a-days, owing to the advancement of medical science. Microarray gene data helps in diagnosing diseases quite accurately. However, processing microarray gene data is difficult and is usually not understandable. Taking this challenge into account, this work presents a user-friendly rule based classification model, which is easily understandable and does not demand users to have prior knowledge. The classification rules are formed with the help of cuckoo search optimization algorithm and the rules are pruned by the associative rule mining. Finally, the classification is performed with the help of the pruned rules. The performance of the proposed approach is satisfactory in terms of accuracy, sensitivity, specificity and time consumption.

Automatic microarray image segmentation with clustering-based algorithms

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.