Motivation: DNA microarrays are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides. Image analysis is an important aspect of microarray experiments. The aim of this step is to reduce an image of spots into a table with a measure of the intensity for each spot. Efficient, accurate and automatic analysis of DNA spot images is essential in order to use this technology in laboratory routines. Results: We present an automatic non-supervised set of algorithms for a fast and accurate spot data extraction from DNA microarrays using morphological operators which are robust to both intensity variation and artefacts. The approach can be summarised as follows. Initially, agridding algorithm yields the automatic segmentation of the microarray image into spot quadrants which are later individually analysed. Then the analysis of the spot quadrant images is achieved in five steps. First, a prequantification, the spot size distribution law is calculated. Second, the background noise extraction is performed using a morphological filtering by area. Third, an orthogonal grid provides the first approach to the spot locus. Fourth, the spot segmentation or spot boundaries definition is carried out using the watershed transformation. And fifth, the outline of detected spots allows the signal quantification or spot intensities extraction; in this respect, a noise model has been investigated. The performance of the algorithm has been compared with two packages: ScanAlyze and Genepix, showing its robustness and precision.

This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which genes are to be printed on the arrays, which sources of RNA are to be hybridized to the arrays and on how many arrays the hybridizations will be replicated. Secondly, after hybridization, there follows a number of data-cleaning steps or `low-level analysis' of the microarray data. The microarray images must be processed to acquire red and green foreground and background intensities for each spot. The acquired red/green ratios must be normalized to adjust for dye-bias and for any systematic variation other than that due to the differences between the RNA samples being studied. Thirdly, the normalized ratios are analyzed by various graphical and numerical means to select differentially expressed genes or to find groups of genes whose expression profiles can reliably classify the different RNA sources into meaningful groups. The sections of this article correspond roughly to the various analysis steps. The following notation will be used throughout the article. The foreground red and green intensities will be written Pp and 9p for each spot. The background intensities will be Pf and 9f . The background-corrected intensities will be P and 9 where usually P Pp Pf 0 # and 9 9p 9f 0 # . The log-differential expression ratio will be vyq # E P 9 0 for each spot. Finally, the log-intensity of the spot will be vyq 3 P9 0 , a measure of the overall brightness of the spot. (The letter E is a mnemonic for minus as vyq vyq E P 9 0 # while 3 is a mnemonic for add as #vyq vyq #...

Abstract: The paper shortly reviews statistical methods used in the area of DNA microarray studies. All stages of the experiment are taken into account: planning, data collection, data preprocessing, analysis and validation. Among the methods of data analysis, the algorithms for estimating differential expression, multivariate approaches, clustering methods, as well as classification and discrimination are reviewed. The need is stressed for routine statistical data processing protocols and for the search of links of microarray data analysis with quantitative genetic models. Key words: data analysis, data collection, DNA microarrays, planning experiments, statistical methods, validation.

We developed a microarray image analysis system which works specifically on nylon membrane microarray. Our system can handle (i) automatic image alignment and gridding, (ii) spot contour detection, and (iii) intensity measurement. The alignment and gridding system is automated with possible gridding provided for microarray images. In spot contour detection, we apply the multiple-snake method, which is the high-level segmentation method, to automatically extract the contours of multiple spots. We tested the system on various designs of microarray images, and we show how the spot intensity is computed. The reliability of the system is determined by comparing the results of duplicated pairs of spots. We also tested the system with glass slide microarray images, and the results are very encouraging. 1.

Abstract. DNA Microarrays are powerful techniques that are used to analyze the expression of DNA in organisms after performing experiments. One of the key issues in the experimental approaches that utilize microarrays is to extract quantitative information from the spots, which represent the genes in the experiments. In this process, separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this paper, we present an optimized clustering-based microarray image segmentation approach. As opposed to traditional clustering-based methods, we use more than one feature to represent the pixels. The experiments show that our algorithm performs microarray image segmentation more accurately than the previous clustering-based microarray image segmentation methods, and does not need a post-processing stage to eliminate the noisy pixels. 1.

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for cDNA microarray experiments. The approach overcomes several limitations of existing methods. In particular it a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively-common ”doughnut-shaped ” spots; b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of corresponding background if the two are estimated separately; c) uses a probabilistic modelling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the Expectation-Maximisation (EM) and the Iterated Conditional Modes (ICM) algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

This article has been sent to Open Mind Journals for publication. When accepted for publication this version may be removed. The applications and development of microarray technology have been growing exponentially in the past few years, since their discovery in 1994. There are numerous applications of this technology, including clinical diagnosis and treatment, drug design and discovery, tumor detection, and in the environmental health research. One of the key issues in the experimental approaches that utilize microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are quite important and influential in future steps of the analysis. Thus, identifying the spots and separating background from the foreground is a fundamental problem in DNA microarray data analysis. In this paper, we present an overview of the state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, the adaptive shape segmentation, the histogram-based methods, and the recently introduced clustering-based techniques. We analytically show that the latter is equivalent to the one-dimensional standard well-known k-means clustering algorithm that utilizes the Euclidean distance.

With the recent explosion of interest in microarray technology, massive amounts of microarray images are currently being produced. The storage and the transmission of this type of data are becoming increasingly challenging. Here we propose lossless and lossy compression algorithms for microarray images originally digitized at 16 bpp (bits per pixels) that achieve an average of 9.5--11.5 bpp (lossless) and 4.6--6.7 bpp (lossy, with a PSNR of 63 dB). The lossy compression is applied only on the background of the image, thereby preserving the regions of interest. The methods are based on a completely automatic gridding procedure of the image.

In this paper, a precise and fully-automatic approach to the determination of the grid alignment (Gridding) on microarray images is presented. The proposed approach is compared to state-of-the-art software programs and techniques. The conducted experiments demonstrate that it is very effective even when it is applied to noisy or distorted images as well as to images containing spots with various intensities. 1.

major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully-automatic approach for accurately locating a distorted grid structure in a microarray image is presented. The gridding process is expressed as an optimization problem which is solved by using a Genetic Algorithm. The Genetic Algorithm determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94 % and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.