S02.4: Methods for automatic microarray image analysis

Katzer, Mathias; Kummert, Franz; Sagerer, Gerhard

doi:10.1002/bimj.200490100

Cited by 4 publications

(7 citation statements)

References 1 publication

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“…The first "core" component that finds grid lines is (a) based on statistical analysis of 1D image projections [34][35][36][37], or (b) used as part of image segmentation algorithms [38][39][40]. The algorithmic approach based on 1D image projections consists of the following steps [24,37].…”

Section: Finding Grid Linesmentioning

confidence: 99%

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

Bajcsy

2006

EURASIP J. Adv. Signal Process.

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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.

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Section: Finding Grid Linesmentioning

confidence: 99%

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

Bajcsy

2006

EURASIP J. Adv. Signal Process.

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“…Sample parameters include the number of sub-grids, columns and rows in a sub-grid, row spacing, row and column resolution, tip spacing, and spot width and height. A few recent studies focus on automatic microarray image analysis (Katzer et al 2002) (Jain et al 2002) (Steinfath 2001). A reliable addressing procedure is desirable to ensure the accuracy of the subsequent process discussed below.…”

Section: Addressingmentioning

confidence: 99%

Spot Detection and Image Segmentation in DNA??Microarray Data

Qin

Rueda

Ali

et al. 2005

Applied Bioinformatics

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Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

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“…The template-based approach is the most prevalent in the previous literature and existing software packages, e.g., GenePix Pro by Axon Instruments [7], ScanAlyze [12] or GridOnArray by Scanalytics [8]. To our knowledge, the data-driven approach has been based on statistical analysis of 1D image projections [13], [17], [18] or used as part of image segmentation algorithms [19], [24]. While most of the currently available software packages enable manual template matching [7], [12], [14] by adjusting spot size, spot spacing and grid location, some software products already incorporate an automatic refinement search for a grid location given size and spacing of spots [7], [10].…”

Section: Previous Workmentioning

confidence: 99%

“…In comparison with other data-driven methods, our proposed method is not based on any segmentation optimization [19] and it is different from the methods described in [17], [18] by analyzing 1D projections of directional edge feature images as opposed to original intensity image. This difference makes the algorithm color independent since the edge features are color free.…”

Section: Previous Workmentioning

confidence: 99%

Gridline: Automatic Grid Alignment in DNA Microarray Scans

Bajcsy

2004

IEEE Trans. on Image Process.

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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.

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S02.4: Methods for automatic microarray image analysis

Cited by 4 publications

References 1 publication

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

Spot Detection and Image Segmentation in DNA??Microarray Data

Gridline: Automatic Grid Alignment in DNA Microarray Scans

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