M3G: maximum margin microarray gridding (original) (raw)

Precise Gridding of Microarray Images by Detecting and Correcting Rotations in Subarrays

DNA microarrays are an important tool for massive gene expression profiling and genotyping studies. A microarray consists of thousands of spots that must be scanned and digitized for further analysis. However, the mechanical constraints and other printing issues increase the difficulty in automatic analysis, most of the applications released up to now need different levels of human intervention, which inevitably reduce the efficiency and reproducibility of the entire process. In this paper, we present a precise microarray gridding method, in which potential problems in grid alignment and possible rotation in subarrays are detected and corrected. The gridding process is fully automatic, which eliminates the need for human intervention.

An Efficient Fully Automated Method for Gridding Microarray Images

American Journal of Biomedical Engineering, 2012

DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Gridding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done manually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accuracy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images.

Gridding spot centers of smoothly distorted microarray images

IEEE Transactions on Image Processing, 2000

We use an optimization technique to accurately locate a distorted grid structure in a microarray image. By assuming that spot centers deviate smoothly from a checkerboard grid structure, we show that the process of gridding spot centers can be formulated as a constrained optimization problem. The constraint is equal to the variations of the transform parameter. We demonstrate the accuracy of our algorithm on two sets of microarray images. One set consists of some images from the Stanford Microarray Database; we compare our centers with those annotated in the Database. The other set consists of oligonucleotide images, and we compare our results with those obtained by GenePix Pro 5.0. Our experiments were performed completely automatically.

A Precise and Automatic Gridding Approach to Noise-Affected and Distorted Microarray Images

2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008

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.

A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images

IEEE Access

Genome-wide screening using microarrays of DNA will be of great use in the early diagnosis of diseases such as cancer and HIV. It also makes use of gene discovery, pharmacogenomics, toxicogenomics, and nutrigenomics for other applications. A DNA microarray image lays out an orderly arranged specific gene regions called spots. Microarray image analysis consists primarily of preprocessing, spot area gridding, spot segmentation, and intensity extraction. The first two phases are focused on this work: preprocessing and gridding. The experiment is conducted on real composite cDNA microarray images. A composite microarray image is formed by suitably stacking a red channel image and a green channel image acquired from a microarray experiment either in the RGB domain or in the GRB domain. The blue channel is kept as zero. In order to reduce the challenging problems of microarray images, an efficient preprocessing algorithm is proposed here for these composite images. We have developed a fully automated gridding algorithm integrating global subgrid gridding and local gridding of spots. This technique extracts the structural information namely inter-subgrid spacing, inter-spot spacing and spot center position to achieve efficient gridding. The traits of a microarray image are evaluated using three parameters namely Mean square error, Naturalness quality image evaluator and degree of contrast. The accuracy of the experimental results indicates that this combined preprocessing and gridding technique performs better than existing competitive methods in SIB, GEO, SMD and DeRisi datasets which are most commonly used by the research community for microarray image analysis techniques. INDEX TERMS cDNA, composite microarray images, contrast enhancement, denoising, fully automated gridding, genome-wide monitoring, global gridding, hybridized spots and non-hybridized spots, local gridding, spot region extraction.

Unsupervised SVM-based gridding for DNA microarray images

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2010

This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vector Machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.

Automatic techniques for gridding CDNA microarray images

2008 IEEE International Conference on Electro/Information Technology, 2008

Microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The accuracy of this phase has an important impact on the accuracy and effectiveness of the subsequent gene expression and identification analysis. The processing can be organized mainly into four steps: gridding, spot isolation, segmentation, and quantification. Although several commercial software packages are now available, microarray image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. This paper describes and compares four techniques that perform automatic gridding and spot isolation. The proposed techniques are based on template matching technique, standard deviation, sum, and derivative of these profiles. Experimental results show that the accuracy of the derivative of the sum profile is highly accurate compared to other techniques for good and poor quality microarray images.

An Original Genetic Approach to the Fully Automatic Gridding of Microarray Images

IEEE Transactions on Medical Imaging, 2008

Gridding microarray images remains, at present, a 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.

A generalized methodology for the gridding of microarray images with rectangular or hexagonal grid

Signal, Image and Video Processing, 2015

Microarrays provide a simple way to measure the level of hybridization of known probes of interest with one or more samples under different conditions. The rapid development of microarray technology requires the implementation of smart and flexible algorithms to deal either with the great amount of data or with the variations of the used hardware. In this paper, a generalized methodology for spot addressing and gridding of microarray images is presented. The methodology can cope with both rectangular and hexagonal grids, which are used for the probes placement onto the substrate. Initially, the methodology identifies the structure of the image, and an efficient spot-by-spot approach has been developed for the detection of all spots in the image. The evaluation of the methodology was performed using both rectangular and hexagonal structured images, merged in a single dataset. The methodology results in high accuracy in the spots detection, ranging from 92.8 to 99.8 % depending on the dataset used.

Gridline: Automatic Grid Alignment in DNA Microarray Scans

IEEE Transactions on Image Processing, 2004

We present a new automatic grid alignment algorithm for detecting two-dimensional (2D) arrays of spots in DNA microarray images. Our motivation for this work is the lack of automation in high-throughput microarray data analysis that leads to (a) spatial inaccuracy of located spots and hence inaccuracy of extracted information from a spot, and (b) inconsistency of extracted features due to manual selection of grid alignment parameters. The proposed grid alignment algorithm is novel in the sense that (1) it can detect irregularly row-and columnspaced spots in a 2D array, (2) it is independent of spot color and size, (3) it is general to localize a grid of other primitive shapes than the spot shapes, (4) it can perform grid alignment on any number of input channels, (5) it reduces the number of free parameters to minimum by data driven optimization of most algorithmic parameters and (6) it has a built-in speed versus accuracy tradeoff mechanism to accommodate user's requirements on performance time and accuracy of the results. The developed algorithm also automatically identifies multiple blocks of 2D arrays, as it is the case in microarray images, and compensates for grid rotations in addition to grid translations.