An Automated cDNA Microarray Image Analysis for the determination of Gene Expression Ratios

2021 ◽  
pp. 1-1
Author(s):  
Steffy MARIA Joseph ◽  
P. S. Sathidevi
Keyword(s):  
Gene Expression ◽  
Image Analysis ◽  
Cdna Microarray ◽  
Microarray Image

Microarray Image Analysis and Gene Expression Ratio Statistics

2005 ◽  
pp. 1-21
Author(s):  
Yidong Chen ◽  
Edward R. Dougherty ◽  
Michael L. Bittner ◽  
Paul Meltzer ◽  
Jeffery Trent
Keyword(s):  
Gene Expression ◽  
Image Analysis ◽  
Expression Ratio ◽  
Microarray Image ◽  
Gene Expression Ratio

Microarray Image Analysis and Gene Expression Ratio Statistics

2007 ◽  
pp. 1-19
Author(s):  
Yidong Chen ◽  
Edward R. Dougherty ◽  
Michael L. Bittner ◽  
Paul Meltzer ◽  
Jeffery Trent
Keyword(s):  
Gene Expression ◽  
Image Analysis ◽  
Expression Ratio ◽  
Microarray Image ◽  
Gene Expression Ratio

Segmentation of cDNA Microarray Image Using Fuzzy c-Mean Algorithm and Mathematical Morphology

2011 ◽  
Vol 464 ◽  
pp. 159-162 ◽  
Author(s):  
Zhi Yao Li ◽  
Gui Rong Weng
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Cdna Microarray ◽  
Glass Slide ◽  
Sequence Information ◽  
Microarray Technology ◽  
Segmentation Method ◽  
Microarray Image ◽  
Adaptive Partitioning ◽  
Low Expression

cDNA microarray technology provides an effectual tool to explore the enormous genome. cDNA microarray consists of thousands of gene sequences which are printed on glass slide and these sequence information can be obtained by forming a microarray image. So image analysis is crucial. However, image segmentation is another key point. How to deal with the gene spots which are always comprised with imperfection such as irregular contours, donut shapes, artifact and spots with low expression is important to the robustness of the segmentation method. The paper proposed a method based on fuzzy c-mean algorithm which can effectively avoid the influence of various types of artifacts through adaptive partitioning.

Microarray Image Analysis Using Genetic Algorithm

2016 ◽  
Vol 4 (3) ◽  
pp. 561 ◽  
Author(s):  
Bolem Sivalakshmi ◽  
N. Naga Malleswara Rao
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Contrast Enhancement ◽  
Expression Profiling ◽  
Clustering Algorithms ◽  
Microarray Technology ◽  
Edge Information ◽  
Microarray Image ◽  
Image Contrast Enhancement

<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in extracting edge information in image and finally this edge information is used in gridding of microarray image. Mostly segmentation of microarray image is carried out using clustering algorithms. Clustering algorithms have an advantage that they are not restricted to a particular shape and size for the spots. In this paper, segmentation using Genetic Algorithm by optimizing K-means index and Jm measure is presented. The qualitative analysis shows that the proposed method achieves better segmentation results than K-means and FCM algorithms.</p>

scholarly journals Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 159196-159205
Author(s):  
Bogdan Belean ◽  
Robert Gutt ◽  
Carmen Costea ◽  
Ovidiu Balacescu
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Image Analysis ◽  
Processing Methods ◽  
Expression Levels ◽  
Microarray Image ◽  
Gene Expression Levels

scholarly journals Application of FFDNET for Image Denoising on Microarray Images

2019 ◽  
Vol 8 (3) ◽  
pp. 2691-2694
Keyword(s):  
Gene Expression ◽  
Image Analysis ◽  
Expression Profiling ◽  
Microarray Technology ◽  
Trade Off ◽  
Microarray Image ◽  
Feature Preservation ◽  
Processing Step ◽  
Model Independent ◽  
Trained Network

Microarray technology allows the simultaneous profiling of thousands of genes. Denoising is an important pre-processing step in microarray image analysis for accurate gene expression profiling. In this paper, as FFDNet provides model independent denoising technique, it is been applied on microarray images. FFDNet is validated on AWGN based images and real noisy images trained network. The application is compared with the standard denoising methods. The results revealed that optimal sigma value to efficiently remove noise while preserving details for AWGN based images and real noisy trained methods were 15 and 20 respectively. Overall, the performance of the FFDNet is better compared to other metrics considered in the study as it is flexible, effective and fast. It is also capable to maintain the trade-off between denoising and feature preservation.

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