Microarray Image Processing Using Expectation Maximization Algorithm and Mathematical Morphology

2009 ◽  
Author(s):  
Weng Guirong ◽  
Su Jian
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Microarray Image ◽  
Microarray Image Processing

Complementary DNA Microarray Image Processing Based on the Fuzzy Gaussian Mixture Model

2009 ◽  
Vol 13 (4) ◽  
pp. 419-425 ◽  
Author(s):  
E.I. Athanasiadis ◽  
D.A. Cavouras ◽  
P.P. Spyridonos ◽  
D.T. Glotsos ◽  
I.K. Kalatzis ◽  
...  
Keyword(s):  
Image Processing ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Dna Microarray ◽  
Gaussian Mixture ◽  
Complementary Dna ◽  
Microarray Image ◽  
Microarray Image Processing

cDNA microarray image processing using fuzzy vector filtering framework

2005 ◽  
Vol 152 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Rastislav Lukac ◽  
Konstantinos N. Plataniotis ◽  
Bogdan Smolka ◽  
Anastasios N. Venetsanopoulos
Keyword(s):  
Image Processing ◽  
Cdna Microarray ◽  
Microarray Image ◽  
Microarray Image Processing ◽  
Fuzzy Vector

Microarray image processing by order morphology

2006 ◽  
Vol 2 (2) ◽  
pp. 151-154
Author(s):  
Dong-xiang Fu ◽  
Jun-shan Ma ◽  
Jia-bi Chen ◽  
Lin-lin Hou
Keyword(s):  
Image Processing ◽  
Microarray Image ◽  
Microarray Image Processing

A Multichannel Order-Statistic Technique for cDNA Microarray Image Processing

2004 ◽  
Vol 3 (4) ◽  
pp. 272-285 ◽  
Author(s):  
R. Lukac ◽  
K.N. Plataniotis ◽  
B. Smolka ◽  
A.N. Venetsanopoulos
Keyword(s):  
Image Processing ◽  
Cdna Microarray ◽  
Order Statistic ◽  
Microarray Image ◽  
Microarray Image Processing

scholarly journals Reducing the variability in cDNA microarray image processing by Bayesian inference

2004 ◽  
Vol 20 (4) ◽  
pp. 518-526 ◽  
Author(s):  
N. D. Lawrence ◽  
M. Milo ◽  
M. Niranjan ◽  
P. Rashbass ◽  
S. Soullier
Keyword(s):  
Image Processing ◽  
Bayesian Inference ◽  
Cdna Microarray ◽  
Microarray Image ◽  
Microarray Image Processing

scholarly journals A Fully Automated Method for Noisy cDNA Microarray Image Quantification

2013 ◽  
Vol 11 (3) ◽  
pp. 2330-2340 ◽  
Author(s):  
Islam A. Fouad ◽  
Mai S. Mabrouk ◽  
Amr A. Sharawy
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Local Variation ◽  
Primary Data ◽  
Microarray Experiments ◽  
Microarray Image ◽  
Automated Method ◽  
Innovative Tool ◽  
Microarray Image Processing ◽  
Spot Quantification

DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as a primary data for subsequent analysis. Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification. In this paper, a   simple automated gridding technique is developed with a great effect on noisy microarray images. A segmentation technique based on ‘edge-detection’ is applied to identify the spots and separate the foreground from the background is known as microarray image segmentation. Finally, a quantification technique is used to calculate the gene expression level from the intensity values of the red and green components of the image. Results revealed that the developed methods can deal with various kinds of noisy microarray images, with high  griddingaccuracy of 92.2% for low quality images and 100% for high quality images resulting in better spot quantification to get  more accurate gene expression values. 

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