Paper Info
Title
Automatic analysis of leishmania infected microscopy images via gaussian mixture models
Abstract
This work addresses the issue of automatic organic component detection and segmentation in confocal microscopy images. The proposed method performs cellular/parasitic identification through adaptive segmentation using a two-level Otsu's Method. Segmented regions are divided using a rule-based classifier modeled on a decreasing harmonic function and a Support Vector Machine trained with features extracted from several Gaussian mixture models of the segmented regions. Results indicate the proposed method is able to count cells and parasites with accuracies above 90%, as well as perform individual cell/parasite detection in multiple nucleic regions with approximately 85% accuracy. Runtime measures indicate the proposed method is also adequate for real-time usage.
Year
DOI
Venue
2012
10.1007/978-3-642-34459-6_9
SBIA
Keywords
Field
DocType
segmented region,automatic analysis,adaptive segmentation,harmonic function,leishmania infected microscopy image,gaussian mixture model,support vector machine,parasite detection,runtime measure,automatic organic component detection,confocal microscopy image
Computer vision,Harmonic function,Pattern recognition,Computer science,Segmentation,Support vector machine classifier,Support vector machine,Artificial intelligence,Microscopy,Classifier (linguistics),Machine learning,Mixture model
Conference
Citations 
PageRank 
References 
2
0.39
7
Authors
2
Name
Order
Citations
PageRank
Pedro A. Nogueira1488.20
Luís Filipe Teófilo2356.18