Microarray sub-grid detection: A novel algorithm

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis LtdA novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing

Automating the processing of cDNA microarray images

This work is concerned with the development of an automatic image processing tool for DNA microarray images. This paper proposes, implements and tests a new tool for cDNA image analysis. The DNAs are imaged as thousands of circularly shaped objects (spots) on the microarray image and the purpose of this tool is to correctly address their location, segment the pixels belonging to spots and extract the quality features of each spot. Techniques used for addressing, segmentation and feature extraction of spots are described in detail. The results obtained with the proposed tool are systematically compared with conventional cDNA microarray analysis software tools

A New Gridding Technique for High Density Microarray Images Using Intensity Projection Profile of Best Sub Image

As the technologies for the fabrication of high quality microarray advances rapidly, quantification of microarray data becomes a major task. Gridding is the first step in the analysis of microarray images for locating the subarrays and individual spots within each subarray. For accurate gridding of high-density microarray images, in the presence of contamination and background noise, precise calculation of parameters is essential. This paper presents an accurate fully automatic gridding method for locating suarrays and individual spots using the intensity projection profile of the most suitable subimage. The method is capable of processing the image without any user intervention and does not demand any input parameters as many other commercial and academic packages. According to results obtained, the accuracy of our algorithm is between 95-100% for microarray images with coefficient of variation less than two.  Experimental results show that the method is capable of gridding microarray images with irregular spots, varying surface intensity distribution and with more than 50% contamination. Keywords: microarray, gridding, image processing, gridding accurac

A biomimetic algorithm for the improved detection of microarray features,

One the major difficulties of microarray technology relate to the processing of large and - importantly - error-loaded images of the dots on the chip surface. Whatever the source of these errors, those obtained in the first stage of data acquisition - segmentation - are passed down to the subsequent processes, with deleterious results. As it has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, this contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zero-in" and eventually delimitate the features existent on the microarray surface

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

Enhancement of DNA microarray images using mathematical morphological image processing

DNA microarray images contain spots that represent the gene expression of normal and cancer samples. As there are numerous spots on DNA microarray images, image processing can help in enhancing an image and assisting analysis. The mathematical morphology is proposed to enhance the microarray image and analyse noise removal on the image. This follows an experiment in which the erosion, dilation, opening, closing, white top-hat (WTH) and black top-hat (BTH) operations were applied on a DNA microarray image and its results analysed. Noise was completely removed by the erosion operation and the images were enhanced

Improved Vector Median Filtering Algorithm for High Density Impulse Noise Removal in Microarray Images

The digital images are corrupted by impulse noise due to errors generated in camera sensors, analog-to-digital conversion and communication channels. Therefore it is necessary to remove impulse noise in-order to provide further processing such as edge detection, segmentation, pattern recognition etc. Filtering a noisy image, while preserving the image details is one of the most important issues in image processing. In this paper, we propose a new method for impulse noise removal in Microarray images. The proposed iterative algorithm search for the noise-free pixels within a small neighborhood. The noisy pixel is then replaced with the value estimated from the noise-free pixels. The process continues iteratively until all noisy-pixels of the noisy image are filtered. The performance of the proposed method is tested using impulse noise corrupted microarray images. The experimental results show the proposed algorithm can perform significantly better in terms of noise suppression and detail preservation in microarray images than a number of existing nonlinear techniques

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.</p

A fully automatic gridding method for cDNA microarray images

<p>Abstract</p> <p>Background</p> <p>Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering.</p> <p>Results</p> <p>We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method.</p> <p>Conclusions</p> <p>Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.</p