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.
Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0