Open AccessAutomatic Detection of Erythrocytes in Fishes using Clustering Segmentation and Supervised Learning
Author(s) -
Kleyton Sartori Leite,
Felipe Gomes da Silva,
Bruno do Amaral Crispim,
Felipe Merey,
Alexéia Barufatti Grisolia,
Willian Paraguassu Amorim
Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2019.7620
Subject(s) - cluster analysis , segmentation , computer science , image segmentation , artificial intelligence , mechanism (biology) , pollution , population , human health , contamination , pattern recognition (psychology) , machine learning , biology , ecology , environmental health , medicine , philosophy , epistemology
The growth of urban areas and the population has favored the increase in pollution and consequently the contamination of river waters. This clue has aroused interest in several aspects, mainly related to the fate and possible effects that these contaminants can cause to human health. The analysis of erythrocytes in sh is an efcient mechanism to identify the presence of genetical terations that maybe being caused by emergent contaminants. This article presents a new proposal for automatic identication of erythrocytes in sh using SLIC segmentation approach and connected components, adjusted using supervised learning, and presenting the performance evaluation in different aspects of the image.