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Microscopic Image Processing for the Analysis of Nosema Disease
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Author(s): Soumaya Dghim (Universidad de Las Palmas de Gran Canaria, Spain), Carlos M. Travieso-Gonzalez (Universidad de Las Palmas de Gran Canaria, Spain), Mohamed Salah Gouider (Université de Tunis, Tunisia), Melvin Ramírez Bogantes (Costa Rica Institute of Technology, Costa Rica), Rafael A. Calderon (National University of Costa Rica, Costa Rica), Juan Pablo Prendas-Rojas (Costa Rica Institute of Technology, Costa Rica)and Geovanni Figueroa-Mata (Costa Rica Institute of Technology, Costa Rica)
Copyright: 2019
Pages: 19
Source title:
Histopathological Image Analysis in Medical Decision Making
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, India), Amira S. Ashour (Tanta University, Egypt), Harihar Kalia (Seemantha Engineering College, India), R.T. Goswami (Techno India College of Technology, India)and Himansu Das (KIIT University, India)
DOI: 10.4018/978-1-5225-6316-7.ch002
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Abstract
In this chapter, the authors tried to develop a tool to automatize and facilitate the detection of Nosema disease. This work develops new technologies in order to solve one of the bottlenecks found on the analysis bee population. The images contain various objects; moreover, this work will be structured on three main steps. The first step is focused on the detection and study of the objects of interest, which are Nosema cells. The second step is to study others' objects in the images: extract characteristics. The last step is to compare the other objects with Nosema. The authors can recognize their object of interest, determining where the edges of an object are, counting similar objects. Finally, the authors have images that contain only their objects of interest. The selection of an appropriate set of features is a fundamental challenge in pattern recognition problems, so the method makes use of segmentation techniques and computer vision. The authors believe that the attainment of this work will facilitate the diary work in many laboratories and provide measures that are more precise for biologists.
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