image processing is an important aspect of microarray experiments. Spots segmentation meaning to distinguish the spot signals from background pixels, is a critical step in microarrayimage processing. After analyzing ...
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ISBN:
(纸本)9780769537443
image processing is an important aspect of microarray experiments. Spots segmentation meaning to distinguish the spot signals from background pixels, is a critical step in microarrayimage processing. After analyzing other existing means of microarraysegmentation, a new method based on region growing algorithm, mathematical morphology (MM) filtering and morphological processing is presented. And its corresponding theory and realizable steps are introduced in this paper. The simulations show that the region growing algorithm method for spot imagesegmentation has better performance than the most commonly used segmentation methods including the ScanAlizeTM method and GenePixTM method. The Experimental results are computationally attractive, have excellent performance and can preserve structural information while efficiently suppressing noise in cDNA microarray data.
Objective: DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper...
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Objective: DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper we introduce a supervised method for the segmentation of microarrayimages using classification techniques. The method is able to characterize the pixels of the image as signal, background and artefact. Methods and material: The proposed method includes five steps: (a) an automated gridding method which provides a cell of the image for each spot. (b) Three multichannel vector filters are employed to preprocess the raw image. (c) Features are extracted from each pixel of the image. (d) The dimension of the feature set is reduced. (e) Support vector machines are used for the classification of pixels as signal, background, artefacts. The proposed method is evaluated using both real images from the Stanford microarray database and simulated images generated by a microarray data simulator. The signal and the background pixels, which are responsible for the quantification of the expression levels, are efficiently detected. Results: A quality measure (q(index)) and the pixel-by-pixel accuracy are used for the evaluation of the proposed method. The obtained q(index) varies from 0.742 to 0.836. The obtained accuracy for the real images is about 98%, while the accuracies for the good, normal and bad quality simulated images are 96, 93 and 71%, respectively. The proposed classification method is compared to clustering-based techniques, which have been proposed for microarray image segmentation. This comparison shows that the classification-based method reports better results, improving the performance by up to 20%. Conclusions: The proposed method can be used for segmentation of microarrayimages with high accuracy, indicating that segmentation can be improved using classification instead of clustering. The proposed method is supervised and it can only be used wh
microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarrayimage processing has been implemented. Clustering-base...
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ISBN:
(纸本)9781424441242
microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarrayimage 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 microarrayimage more accurately than the k-means.
microarrays allow the monitoring of expressions for tens of thousands of genes simultaneously. image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identificatio...
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ISBN:
(纸本)9780909925970
microarrays allow the monitoring of expressions for tens of thousands of genes simultaneously. image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes. image processing for microarrayimages includes three tasks: spot gridding, segmentation and information extraction. In this paper, we address the segmentation and information extraction problems, and proposed a new segmentation method based on K-means clustering and a new background and foreground correction algorithm based on mathematical morphological and histogram analysis for information extraction. The advantage of our method is that it does not have any restrictions for the shape of spots. Wecompare our experimental results with those obtained from the popular software GenePix.
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