A multi-window approach to classify histological features

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

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Detail(s)

Original languageEnglish
Pages (from-to)259-262
Journal / PublicationProceedings - International Conference on Pattern Recognition
Volume15
Issue number2
Publication statusPublished - 2000

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DOI

DOI
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Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-0012699624&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(adb7aa80-9bfe-4805-8ca0-ad227f1ad2c0).html

Abstract

Medical images are usually composed of different kinds of texture components which are always so much varied that a conventional single window approach cannot capture enough salient information for comparison. This paper uses the widely used multi-channel Gabor filters to demonstrate how a multi-window approach can improve the classification accuracy rate of histological labels. In addition, a Most Confident Window method will be proposed to further increase the accuracy rate of the multi-window approach. © 2000 IEEE.

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Publication fingerprints text

100
Window Approach Keyphrases
100
Multi-window Keyphrases
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Histopathological F... Keyphrases
100
Channels Computer Science
100
Classification Accu... Computer Science
100
Procedure Computer Science
100
Accuracy Computer Science
100
accuracy INIS
50
filters INIS
50
information INIS
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Citation Format(s)

[ RIS ] [ BibTeX ]
A multi-window approach to classify histological features. / Lam, Ringo W. K.; Ip, Horace H. S.; Cheung, Kent K. T. et al.
In: Proceedings - International Conference on Pattern Recognition, Vol. 15, No. 2, 2000, p. 259-262.

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal