A Review on Gridding Techniques of Microarray Images

Image processing is an important stage of every microarray experiment. Reliability of this stage strongly influences the results of data analysis performed on extracted gene expressions. Multiple methods related to array recognition, spot segmentation and measurement extraction have emerged in this area over past several years. Currently there are various commercial and freeware packages available, which perform microarray image analysis. This paper attempts to review microarray image analysis as a whole and to make some experimental comparison of several computational schemes for signal segmentation and measurement extraction. Also we provide a detailed discussion of automated image quality control for use with microarray images.

<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering. It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms.</p>

Microarray analysis is one of the most suitable tools available for scientists concerned with DNA sequences to study and examine gene expression. Through microarray analysis, the gene expression sequence can be obtained and biological information on many diseases can be acquired. The gene expression information contained in the microarray can be extracted using image-processing techniques. Microarray image processing consists of three main steps: gridding, segmentation and intensity extraction. This paper analyses the computational time for this microarray image processing. The results show that the intensity extraction consumes majority of the overall computational time. More detail analysis reveals that this high computational time is due to the background correction part of the process, as discussed in the second part of this paper.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

Complementary DNA microarray technology is a powerful tool in many areas. Usually a two channel microarray red-green (RG) image is obtained. Due to the nature of cDNA microarray technology, a number of impairments affect the cDNA microarray image before the analysis such as identification of differentially expressed genes. Microarray image processing plays a crucial role in the extraction and quantitative analysis of the relative abundance of the DNA product. In this paper, a method combined K-means clustering algorithm and mathematical morphology is presented. Mathematical morphology is a useful tool for extracting image components. K-means clustering algorithm has a good performance in the segmentation of microarray image processing. The result of the experiment shows that the method presented in this paper is accurate, automatic and robust.

An automated method for gridding and clustering-based segmentation of cDNA microarray images

Optimal multilevel thresholding using bacterial foraging algorithm

Microarray images, which allow the analysis of hybridization expressions of genes, have been one of the most widely used high-throughput technologies with many different applications. Accurate and automatic microarray image analysis is very important since researchers trust the information provided in these experiments and construct conclusions based on the results produced by the software responsible in analyzing the hybridized arrays. Every microarray image contains thousands of spots, so how to do the spots finding in microarray images accurately and automatically is very meaningful. There are always some problems, such as rotation and distortion, in a microarray image caused by mechanical errors and/or optical errors in the system. All these problems will hinder "doi"ng analysis automatically. Early research has worked out several algorithms to deal with the rotation problem, but those algorithms can not handle microarray images with distortions. In this paper, we propose a robust spots finding method to deal with both rotation and/or distortion in microarray images. The proposed method provides automatic gridding and can handle a microarray image with different type of rotation (global or sub-array rotation) and optical distortions.

BackgroundImage analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identified. Then, those target areas have to be partitioned into foreground and background. Finally, two scalar values for the intensities have to be extracted. These goals have been tackled either by spot shape methods or intensity histogram methods, but it would be desirable to have hybrid algorithms which combine the advantages of both approaches.ResultsA new robust and adaptive histogram type method is pixel clustering, which has been successfully applied for detecting and quantifying microarray spots. This paper demonstrates how the spot shape can be effectively integrated in this approach. Based on the clustering results, a bivalence mask is constructed. It estimates the expected spot shape and is used to filter the data, improving the results of the cluster algorithm. The quality measure 'stability' is defined and evaluated on a real data set. The improved clustering method is compared with the established Spot software on a data set with replicates.ConclusionThe new method presents a successful hybrid microarray image analysis solution. It incorporates both shape and histogram features and is specifically adapted to deal with typical microarray image characteristics. As a consequence of the filtering step pixels are divided into three groups, namely foreground, background and deletions. This allows a separate treatment of artifacts and their elimination from the further analysis.

Background/Objectives: This study presents an external and internal prior guided patch based filter for minimizing Gaussian noise in complementary deoxyribonucleic acid (cDNA) microarray images. Methods/Statistical analysis: The proposed denoising filter is capable of taking into consideration both external and internal prior. It employs combined prior guided patch based denoising considering various distance based patch-matching methods. Findings: Experimental results demonstrate that the combined prior guided patch based filter outperforms the existing well-known filters in minimizing noise of cDNA microarray images. The outcome of the proposed scheme found to offer better peak signalto-noise ratio and structural similarity index in contrast to existing filtering techniques. Effectiveness of the proposed denoising method is also assessed by estimating the spot intensities of cDNA microarray image that reflects the effect of noise reduction in the image. Application/Improvements: Minimization of noise is a crucial step of cDNA microarray image processing and it aids in microarray analysis by extracting valid and good quality gene expression measurements. Keywords: Denoising, External Prior, Internal Prior, K- nearest Neighbor, Microarray Image, Singular Value Decomposition

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

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DNA microarray technology has permitted the analysis of global gene expression profiles in clinical diagnosis, treatment, and environmental health research for several diseases including cancer. Microarray images analysis, processing and segmentation are decisive steps in gene expression analysis, since any error leads to improper diagnosis. These techniques increased and enhanced over the past few years. However, the segmentation of the unshaped spots within the microarray image is still a problem due to the variations of spots in shapes and sizes. Hence, we introduce a new unshaped microarray spot segmentation model, which focuses on spots detection and segmentation regardless of their size and shape using fractal dimensions transform. Real microarray images from the Stanford Microarray Images Database (SMD) and Universal Microarray Images Database (UNC) are used to evaluate the efficiency of the proposed model. The numerical and visual results show that the proposed model improves the accuracy and the efficiency of the spots segmentation process. Biological researchers can use this model in their labs and clinics, as a free priceless software instead of using expensive ones like ScanAlyze, GenePix or spot software.