GridWeaver: A Fully-Automatic System for Microarray Image Analysis Using Fast Fourier Transforms

Experiments using microarray technology generate large amounts of image data that are used in the analysis of genetic function. An important stage in the analysis is the determination of relative intensities of spots on the images generated. This paper presents GridWeaver, a program that reads in images from a microarray experiment, automatically locates subgrids and spots in the images, and then determines the spot intensities needed in the analysis of gene function. Automatic gridding is performed by running Fast Fourier Transforms on pixel intensity sums. Tests on several data sets show that the program responds well even on images that have significant noise, both random and systemic

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

Fully automatic classification of breast cancer microarray images

AbstractA microarray image is used as an accurate method for diagnosis of cancerous diseases. The aim of this research is to provide an approach for detection of breast cancer type. First, raw data is extracted from microarray images. Determining the exact location of each gene is carried out using image processing techniques. Then, by the sum of the pixels associated with each gene, the amount of “genes expression” is extracted as raw data. To identify more effective genes, information gain method on the set of raw data is used. Finally, the type of cancer can be recognized via analyzing the obtained data using a decision tree. The proposed approach has an accuracy of 95.23% in diagnosing the breast cancer types

A New Method of Gridding for Spot Detection in Microarray Images

A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. The gridding process is usually divided into two main steps: sub-gridding and spot detection. In this paper, a fully automatic approach to detect the location of spots is proposed. Each spot is associated with a gene and contains the pixels that indicate the level of expression of that particular gene. After gridding, the image is segmented using fuzzy c-means clustering algorithm for separation of spots from the background pixels.  The result of the experiment shows that the method presented in this paper is accurate and automatic without human intervention and parameter presetting. Keywords: Microarray Image, Mathematical Morphology, Image Processin

Automatic Gridding for DNA Microarray Image Using Image Projection Profile

DNA microarray is powerful tool and widely used in many areas. DNA microarray is produced from control and test tissue sample cDNAs, which are labeled with two different fluorescent dyes. After hybridization using a laser scanner, microarray images are obtained. Image analysis play an important role in extracting fluorescence intensity from microarray image. First step in microarray image analysis is addressing, that is finding areas in the image on which contain one spot using gird lines. This step can be done by either manually or automatically. In this paper we propose an efficient and simple automatic gridding for microarray image analysis using image projection profile, base on fact that microarray image has local minimum and maximum intensity at background and foreground areas respectively. Grid lines are obtained by finding local minimum of vertical and horizontal projection profile. This algorithm has been implemented in MATLAB and tested with several microarray image

Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research

Automatic gridding of DNA microarray images.

Microarray (DNA chip) technology is having a significant impact on genomic studies. Many fields, including drug discovery and toxicological research, will certainly benefit from the use of DNA microarray technology. Microarray analysis is replacing traditional biological assays based on gels, filters and purification columns with small glass chips containing tens of thousands of DNA and protein sequences in agricultural and medical sciences. Microarray functions like biological microprocessors, enabling the rapid and quantitative analysis of gene expression patterns, patient genotypes, drug mechanisms and disease onset and progression on a genomic scale. Image analysis and statistical analysis are two important aspects of microarray technology. Gridding is necessary to accurately identify the location of each of the spots while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment that is used to print the arrays, rotations, misalignments, high contaminations with noise and artifacts, solving the grid segmentation problem in an automatic system is not trivial. The existing techniques to solve the automatic grid segmentation problem cover only limited aspect of this challenging problem and requires the user to specify or make assumptions about the spotsize, rows and columns in the grid and boundary conditions. An automatic gridding and spot quantification technique is proposed, which takes a matrix of pixels or a microarray image as input and makes no assumptions about the spotsize, rows and columns in the grid and is found to effective on datasets from GEO, Stanford genomic laboratories and on images obtained from private repositories. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .V53. Source: Masters Abstracts International, Volume: 43-03, page: 0891. Adviser: Luis Rueda. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

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

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