A New Method for DNA Microarray Image Segmentation

2005 ◽  
pp. 886-893 ◽  
Author(s):  
Luis Rueda ◽  
Li Qin
Keyword(s):  
Image Segmentation ◽  
Dna Microarray ◽  
New Method ◽  
Microarray Image

scholarly journals DNA Microarray Image Segmentation Using Markov Random Field Algorithm

2021 ◽  
Vol 2071 (1) ◽  
pp. 012032
Author(s):  
K A Mat Said ◽  
A B Jambek
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Random Field ◽  
Dna Microarray ◽  
Markov Random Field ◽  
Deoxyribonucleic Acid ◽  
Experimental Results ◽  
Microarray Image ◽  
Markov Random ◽  
Stage Process

Abstract A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

A new method for image segmentation

1989 ◽  
Vol 46 (1) ◽  
pp. 82-95 ◽  
Author(s):  
S.D. Yanowitz ◽  
A.M. Bruckstein
Keyword(s):  
Image Segmentation ◽  
New Method

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

scholarly journals Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Jun-yi Li ◽  
Yi-ding Zhao ◽  
Jian-hua Li ◽  
Xiao-jun Liu
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Communication Pattern ◽  
Communication Model ◽  
Von Neumann ◽  
Bee Colony ◽  
Segmentation Problem

This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.

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