A Study on Image Enhancement and Noise Filtering of Kato-Katz Images

Sinada, F., Reni, S. and Kale, I. 2024. A Study on Image Enhancement and Noise Filtering of Kato-Katz Images. Lecture Notes in Networks and Systems. 1000, pp. 277-290. https://doi.org/10.1007/978-981-97-3289-0_23

TitleA Study on Image Enhancement and Noise Filtering of Kato-Katz Images
TypeJournal article
AuthorsSinada, F., Reni, S. and Kale, I.
Abstract

Neglected Tropical Diseases affect more than 24% of the world’s poorest populations. Some NTDs are diagnosed using kato-katz images; the kato-katz technique is the most commonly used diagnostic technique for soil-transmitted helminthiasis. The technique involves microscopic examination of sieved stool samples and tallying the parasitic eggs present. Kato-katz images present a unique and complex problem when it comes to image enhancement for feature detection. The foreground objects of interest (the parasite eggs), have features that match with the rest of the foreground objects in the image, making it difficult to distinguish and differentiate them. Hence sensible noise filtering and feature enhancement strategies are required for successful object identification and diagnosis. This paper will describe the results of studies on noise filtering and image enhancement of kato-katz images. Due to the ‘noisy’ and ‘noise-like’ nature of the images and the large number of objects in the images, typical noise filtering methods produce poor results. To our best knowledge, this is a pioneering work looking at image enhancement and noise filtering of kato-katz images. This work builds upon previous research by the authors concerning kato-katz images

Keywords Kato-katz Images
Image Feature Extraction
Image Enhancement
Noise Filtering
Neglected Tropical Diseases
JournalLecture Notes in Networks and Systems
Journal citation1000, pp. 277-290
ISSN2367-3370
Year2024
PublisherSpringer
Accepted author manuscript
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-97-3289-0_23
Publication dates
Published02 Aug 2024

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