cDNA Microarray Genome Image Processing Using Fixed Spot Position

Alhadidi, Basim; Fakhouri, Hussam N.; AlMousa, Omar S.

doi:10.3844/ajassp.2006.1730.1734

Cited by 21 publications

(11 citation statements)

References 6 publications

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“…Fig. 1: a. RGB color microarray image, b. Grayscale microarray image Gridding is performed in an automated way assuming the positions of spots are fixed [22]. Although the size and intensity of each spot is different from each other, the spots are located on the microarray image in an order.…”

Section: Resultsmentioning

confidence: 99%

Clustering-based spot segmentation of cDNA microarray images

Uslan

Bucak

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background. The experiments show that fuzzy c-means have segmented spots of the microarray image more accurately than the k-means.

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Section: Resultsmentioning

confidence: 99%

Clustering-based spot segmentation of cDNA microarray images

Uslan

Bucak

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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“…This applicable method can correctly grid all the four types of microarray images without any human intervention. By comparing our method with other methods as those implemented by Deepa J and Tessamma Thomas [20] and Basim Alhadidi [21], it was found that our method is more accurate and can grid various types of noisy images correctly as it mainly deals with noisy images. It should be noted that the processing time for the gridding of the four various types of microarray images, was lower than 6 sec.…”

Section: A Very Good Imagementioning

confidence: 96%

Developing a new methodology for de-noising and gridding cDNA microarray images

Fouad

Labib

Sharawy

et al. 2012

2012 Cairo International Biomedical Engineering Conference (CIBEC)

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DNA Microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. In microarray experiment, the gene expression process is divided into three basic steps: gridding, segmentation, and quantification. For processing of microarray images, a new, automatic, fast and accurate approach is proposed for gridding noisy cDNA microarray images. In the real world, microarray image doesn't reflect measures of the fluorescence intensities for the dye of interest only, as different kinds of noise and artifacts can be observed. In this paper, a novel gridding method based on projection is developed accompanied by a pre-processing, postprocessing, and refinement steps for noisy microarray images. Results revealed that the proposed method is used with high accuracy and minimal processing time and can be applied to various types of noisy microarray images.

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“…The adaptive shape segmentation segments a spots by its shape either by the seeded region growing (SRG) ( Bariamis et al, 2010) or the globally optimal geodesic active contours (GOGAC) (Alhadidi et al, 2006). The spot is adaptive in size and can be of irregular shape.…”

Section: Literature Reviewmentioning

confidence: 99%

New methodology for microarray spot segmentation and gene expression analysis

Hudaib¹,

Fakhouri²,

Ghnemat³

2016

Sci. Res. Essays

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DNA microarray analysis is the main core in genome mapping. Each microarray image contains millions of information about genes. Microarray analysis is considered one of the most recent and important technologies in exploring the genome. One of the key steps in microarray analysis is to extract gene information from the gene spots, these information represent gene expression levels in the microarray. This paper proposes a new methodology to improve microarray spot analysis based on spot extracted segments. It concentrates on each spot segment area independently rather than analyzing all the spots area together of the microarray image. This paper provides a formal model to enhance the intensity values obtained from gene expression levels of the microarray at any intensity expressed level. It also this paper presents the adaptive threshold techniques to be used for microarray segmentation. The experimental results show that the mean of the gene expression intensity value was 87.77.

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cDNA Microarray Genome Image Processing Using Fixed Spot Position

Cited by 21 publications

References 6 publications

Clustering-based spot segmentation of cDNA microarray images

Clustering-based spot segmentation of cDNA microarray images

Developing a new methodology for de-noising and gridding cDNA microarray images

New methodology for microarray spot segmentation and gene expression analysis

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