Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2707

Full Length Research Paper

Intelligent computer vision system for Sprague-Dawley rat sperm classification using gray-level microscopic images

Samy A. Salamah1, Nor Ashidi Mat Isa1*, Khairunnisa Hasikin1,2 and Umi Kalthum Ngah1
1Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia. 2Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia.
Email: [email protected]

  •  Accepted: 07 March 2012
  •  Published: 31 March 2012

Abstract

This paper presents a development of intelligent computer vision system for Sprague-Dawley rat sperm classification. The system is developed to help pathologists and improve the current conventional detection methods and classification of Sprague-Dawley rat sperm. The conventional method tends to result in errors and is also time consuming. Thus, the new computer vision system is developed to overcome these problems and, consequently, produce more accurate results. The system is capable of classifying rat sperm into two groups, namely, normal and abnormal, based on the morphological characteristics of the sperm head. Furthermore, the system has the ability to classify the shapes of abnormal sperms which are banana and hookless shapes. The new system consists of four stages. In the first stage, sperms are segmented using the adaptive fuzzy moving K-means (AFMKM) clustering algorithm. In the second stage, a new automatic cropping algorithm was used to isolate the sperm head and to exclude other details. In the third stage, the feature extraction process was performed, wherein five features namely curvature angle, two flattened angles, curvature depth and number of curvatures are proposed to classify the sperm head of the rat. In the fourth (final) stage, the sperm head is classified using hybrid multilayered perceptron (HMLP) neural network trained with modified recursive prediction error (MRPE) algorithm. The developed system achieved an accuracy rate of 98.7%.

 

Key words: Sprague-Dawley rat sperm, adaptive fuzzy moving K-means clustering algorithm, hybrid multilayered perceptron network, modified recursive prediction error algorithm.

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