A Combinational Fuzzy Clustering Approach for Microarray Spot Segmentation

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Author(s): Ong Pauline (Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia)and Zarita Zainuddin (Universiti Sains Malaysia (USM), Malaysia)
Copyright: 2017
Pages: 19
Source title: Intelligent Multidimensional Data Clustering and Analysis
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Sourav De (Cooch Behar Government Engineering College, India), Indrajit Pan (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-5225-1776-4.ch012

Keywords: Data Analysis & Statistics / Data Analysis and Statistics / Information Science Reference / Library & Information Science

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Abstract

Due to microarray experiment imperfection, spots with various artifacts are often found in microarray image. A more rigorous spot recognition approach in ensuring successful image analysis is crucial. In this paper, a novel hybrid algorithm was proposed. A wavelet approach was applied, along with an intensity-based shape detection simultaneously to locate the contour of the microarray spots. The proposed algorithm segmented all the imperfect spots accurately. Performance assessment with the classical methods, i.e., the fixed circle, adaptive circle, adaptive shape and histogram segmentation showed that the proposed hybrid approach outperformed these methods.

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A Combinational Fuzzy Clustering Approach for Microarray Spot Segmentation

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