NDLI: Performance of texture descriptors in classification of medical images with outsiders in database

Content Provider	IEEE Xplore Digital Library
Author	Avramovic, A. Marovic, B.
Copyright Year	2012
Description	Author affiliation: Faculty of Electrical Engineering, University of Banja Luka, Bosnia and Herzegovina (Avramovic, A.) \|\| Belgrade University Computer Centre, Serbia (Marovic, B.)
Abstract	During the years image classification gained important significance in practice, especially in the fields of digital radiology, remote sensing, image retrieval, etc. Typical algorithm for image classification contains descriptor extraction phase, learning phase and testing phase. Testing phase calculates accuracy of the classifier based on predetermined set of labelled images. This paper analyse performance of texture descriptors combined with SVMs, in the case when test dataset contains images not belonging to any predetermined class. A robustness of texture descriptors on outsiders is analysed, to see if descriptor is able to separate outsiders in specific class. Medical dataset containing various radiology images is used for testing. It was shown that it is possible to separate images not belonging to any class with cost of decreased performance by few percent.
Starting Page	209
Ending Page	212
File Size	702139
Page Count	4
File Format	PDF
ISBN	9781467315692
e-ISBN	9781467315722
DOI	10.1109/NEUREL.2012.6420013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-20
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Feature extraction Image segmentation Biomedical imaging Image retrieval Vectors classification Radiology images texture
Content Type	Text
Resource Type	Article

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