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

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

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 of microarray images based on spatial constrained K-means and Voronoi diagrams

Images from complementary DNA (cDNA) microarrays need to be processed automatically due to the huge amount of information that they provide. In addition, automatic processing is also required to implement batch processes able to manage large image databases. Most of existing softwares for microarray image processing are semiautomatic, and they usually need user intervention to select several parameters such as positional marks on the grids, or to correct the results of different stages of the automatic processing. On the other hand, many of the available automatic algorithms fail when dealing with rotated images or misaligned grids. In this work, a novel automatic algorithm for cDNA image gridding based on spatial constrained K-means and Voronoi diagrams is presented. The proposed algorithm consists of several steps, viz., image denoising by means of median filtering, spot segmentation using Canny edge detector and morphological reconstruction, and gridding based on spatial constrained K-means and Voronoi diagrams computation. The performance of the algorithm was evaluated on microarray images from public databases yielding promising results. The algorithm was compared with other existing methods and it shows to be more robust to rotations and misalignments of the grids.Red de Universidades con Carreras en Informática (RedUNCI

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

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

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

AUTOMATIC GRIDDING CITRA MICROARRAY DENGAN MENGGUNAKAN IMAGE THRESHOLDING

.Citra microarray adalah citra hasil pemindaian laser scanner terhadap microarray yang umumnya digunakan untuk mendeteksi perbedaan efek hibridisasi dari dua kelompok sampel DNA. Citra tersebut kemudian dianalisis untuk mendapatkan intensitas fluorescence setiap titik mikroskopis DNA pada microarray. Salah satu tahapan analisis citra microarray adalah menentukan daerah pada citra microarray yang memuat satu titik mikroskopis DNA. Penentuan daerah ini dapat dilakukan secara manual maupun secara otomatis atau yang dikenal dengan automatic gridding. Beberapa penelitian menggunakan k-mean clustering untuk melakukan automatic gridding, tetapi metode ini membutuhkan waktu komputasi yang cukup lama. Dalam makalah ini akan dipaparkan metode untuk automatic gridding dengan menggunakan image thresholding. Selain itu juga dilakukan simulasi dengan menggunakan MATLAB untuk membandingkan waktu yang diperlukan untuk komputasi automatic gridding dengan image thresholding dan automatic gridding dengan k-mean clustering. Hasil simulasi menunjukkan bahwa waktu komputasi automatic gridding dengan image thresholding jauh lebih sedikit di bandingkan dengan automatic gridding dengan k-mean clustering