Methods for Segmentation of Microarray Image: A Review

Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. 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, seg...

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Bibliographic Details
Published in:International journal of computer science and information security 2016-10, Vol.14 (10), p.690
Main Authors: Kumar, G Sai Chaitanya, Kumar, Reddi Kiran, Naidu, G Apparao
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Experiments
Gene expression
Methods
Quantitative analysis
Online Access:Get full text
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Summary:Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. 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 a review of segmentation methods on microarray image. Segmentation can be done using different methods such as Histogram Thresholding, Region growing and merging, Edge detection and Clustering algorithms. 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. The qualitative and quantitative analysis shows that segmentation using Fuzzy c-means clustering algorithm provides better segmentation result.
ISSN:1947-5500