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Diagnosis of Vulvovaginal Candidiasis Via Automatic Extraction of Candida Fungus From Pap Smear Images



Momenzadeh M1 ; Talebi A2 ; Mehridehnavi A3 ; Rabbani H3
Authors
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Authors Affiliations
  1. 1. Department of Bioelectric and Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Isfahan Medical School Published:2014

Abstract

Background: Vulvovaginal candidiasis (VVC) is a common clinical problem due to occurrence overgrowth of candida in genital system mucosa of females. The aim of this study was automatic diagnosis of vulvovaginal candidiasis via detection and extraction of candida fungus from microscopic images of Pap smear samples. We used image processing techniques to detect candida fungus. Methods: The sample space consisted of 200 microscopic images. Microscopic images were prepared from 49 Pap smear samples using Nikon1 V1 camera mounted on Nikon Eclipse 50i light microscope. For uniform illumination of the images, bottom-hat filtering was used. De-correlation stretching and linear contrast stretching were used for contrast enhancement. Different geometric features such as area, major axis, minor axis, eccentricity, perimeter, compactness, and decision tree classifier were used for extraction of mycelium and conidium of candida. Findings: The results of extraction of mycelium showed a specificity of 98.64% and a sensitivity of 96.88%. The corresponding values for conidium detection were 91.54% and 92.32%, respectively. Conclusion: According to our findings, this software would be helpful to pathologists in the diagnosis of vulvovaginal candidiasis in prevention of eyestrain. It could increase the accuracy of diagnosis, too. © 2014, Isfahan University of Medical Sciences(IUMS). All rights reserved.
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