Towards an automated medical diagnosis system for intestinal parasitosis

Human parasites are a real public health problem in tropical countries, especially in underdeveloped countries. Usually, the medical diagnosis of intestinal parasites is carried out in the laboratory by visual analysis of stools samples using the optical microscope. The parasite recognition is reali...

Full description

Saved in:
Bibliographic Details
Published in:Informatics in medicine unlocked 2018-01, Vol.13, p.101-111
Main Authors: Tchinda, Beaudelaire Saha, Noubom, Michel, Tchiotsop, Daniel, Louis-Dorr, Valerie, Wolf, Didier
Format: Article
Language:English
Subjects:
Active contours
Automatic
Engineering Sciences
Hough transform
Intestinal parasites
Probabilistic neural network
Wavelet edge detector
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Human parasites are a real public health problem in tropical countries, especially in underdeveloped countries. Usually, the medical diagnosis of intestinal parasites is carried out in the laboratory by visual analysis of stools samples using the optical microscope. The parasite recognition is realized by comparing its shape with the known forms. We offer a solution to automate the diagnosis of intestinal parasites through their images obtained from a microscope connected directly to a computer. Our approach exploits the contour detection based on the multi-scale wavelet transform for detecting the parasite. Active contours are combined with the Hough transform to perform image segmentation and extraction of the parasite. We used the principal component analysis for the extraction and reduction of features obtained directly from pixels of the extracted parasite image. Our classification tool is based on the probabilistic neural network. The obtained algorithms were tested on 900 samples of microscopic images of 15 different species of intestinal parasites. The result shows a 100% recognition rate of success. [Display omitted]
ISSN:2352-9148
2352-9148
DOI:10.1016/j.imu.2018.09.004