DocumentCode :
1647828
Title :
Automatic classification of Nosema pathogenic agents through machine vision techniques and kernel-based vector machines
Author :
Alvarez-Ramos, C.M. ; Nino, E. ; Santos, Marcos
Author_Institution :
Fac. de Ing. y Cienc. Basicas, Politec. Grancolombiano, Bogota, Colombia
fYear :
2013
Firstpage :
1
Lastpage :
5
Abstract :
Over the past few years, the microscopic image analysis has become increasingly important for the diagnosis and classification of diseases in natural and health sciences. Although some computational tools are available for image processing on those areas, their efficiency is limited by lack of adaptation to the specific problem. This work presents a simple and direct method to identify and classify spores with the use of machine vision and supervised learning techniques in order to detect diseases in bee colonies. The method makes use of segmentation techniques to identify spores which are subsequently classified by means of multi-class kernel-based vector machines. Different computer vision tools have been combined and applied to enhance the images and get the relevant information. The results are encouraging and are also applicable to the diagnosis of other parasitic diseases.
Keywords :
bioinformatics; computer vision; diseases; image classification; image enhancement; image segmentation; learning (artificial intelligence); microorganisms; object recognition; support vector machines; automatic Nosema pathogenic agent classification; bee colonies; computer vision; disease classification; disease detection; disease diagnosis; health science; image enhancement; image processing; machine vision technique; microscopic image analysis; multiclass kernel-based vector machines; natural science; parasitic disease; segmentation techniques; spore classification; spore identification; supervised learning techniques; Databases; Diseases; Feature extraction; Image segmentation; Object segmentation; Support vector machines; Multi-class SVMs; automatic diagnosis; bees; bioinformatics; biomedical image processing; machine vision; object identification; parasitic diseases; supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing Colombian Conference (8CCC), 2013 8th
Conference_Location :
Armenia
Print_ISBN :
978-1-4799-1054-0
Type :
conf
DOI :
10.1109/ColombianCC.2013.6637516
Filename :
6637516
Link To Document :
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