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Erschienen in: Neural Computing and Applications 6/2012

01.09.2012 | Original Article

Leukocyte image segmentation by visual attention and extreme learning machine

verfasst von: Chen Pan, Dong Sun Park, Yong Yang, Hyouck Min Yoo

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

This paper presents a fast and simple framework for leukocyte image segmentation by learning with extreme learning machine (ELM) and sampling via simulating visual system. In sampling stage, visual attention and the effect of microsaccades in fixation are simulated. The high gradient pixels in fixation regions are sampled to group training set. We designed an automatic sampling process for leukocyte image according to the staining knowledge of blood smears. In learning stage, ELM classifier is trained online to simulate visual neuron system and then extracts pixels of object from image. The ELM-based segmentation is fully automatic by the proposed framework, which could find efficient samples actively, train the classification model in real time and almost no parameter adjusted. Experimental results demonstrated the new method could extract entire leukocyte from complex scenes, has equivalent performance compared to the SVM-based method and exceeds the marker-controlled watershed algorithm.

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Fußnoten
1
The doctor came from the pathology laboratory of Fourth Military Medical University, Xi’an, China.
 
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Metadaten
Titel
Leukocyte image segmentation by visual attention and extreme learning machine
verfasst von
Chen Pan
Dong Sun Park
Yong Yang
Hyouck Min Yoo
Publikationsdatum
01.09.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0522-9

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