NDLI: Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform

Content Provider	Springer Nature Link
Author	Tchiotsop, Daniel Saha Tchinda, Beaudelaire Tchinda, René Kenné, Godpromesse
Copyright Year	2014
Abstract	Biomedical image processing is experiencing a significant progress with many applications. However, automatic recognition of microscopic pathogens from their images remains a challenge that will allow clinical laboratories to increase both the speed of tests and the reliability of diagnoses. We present an algorithm for edge detection of parasites in microscopic images of stools, using the multi-scale wavelet transform. This method is an evolution of the Canny–Mallat detector which gives the possibility to vary the frequency of the analysis in order to find the outlines of the most significant edges. The various contours obtained are chained across the scales from the coarsest to the finest. Using this algorithm, we were able to correctly represent the contours of the features of parasites found in microscopic images. The results obtained were compared with those produced by classical edge detectors on the same images. It comes out from both subjective and objective quantitative performances evaluation that our detector, better than all others, can clearly mark the outlines of the structures of the pathogen on an image of stools.
Starting Page	121
Ending Page	134
Page Count	14
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	9
Issue Number	1
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2014-11-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Automatic medical diagnosis Edge detection Intestinal parasites’ images Parasites shapes’ features Wavelet transform Performance evaluation Signal, Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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