A robust technique based on invariant moments - ANFIS for recognition of human parasite eggs in microscopic images

  • Authors:

  • Esin Dogantekin;Mustafa Yilmaz;Akif Dogantekin;Engin Avci;Abdulkadir Sengur

  • Affiliations:

  • Firat University, Firat Medicine Center, Department of Microbiology and Clinical Microbiology, 23119 Elazig, Turkey;Firat University, Firat Medicine Center, Department of Microbiology and Clinical Microbiology, 23119 Elazig, Turkey;Firat University, Firat Medicine Center, Department of Internal Diseases, 23119 Elazig, Turkey;Firat University, Department of Electronic and Computer Science, 23119 Elazig, Turkey;Firat University, Department of Electronic and Computer Science, 23119 Elazig, Turkey

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

In this study, we propose a robust technique based on invariant moments - adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.