Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition
Metadata of CSIR Papers
View Archive Info| Field | Value | |
| Title |
Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition
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| Creator |
Mukherjee, A
Paul, RR Chaudhuri, K Chatterjee, J Pal, M Banerjee, P Mukherjee, K Banerjee, S Dutta, PK |
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| Subject |
Oncology; Dentistry, Oral Surgery & Medicine
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| Description |
This paper presents an automatic method for classification of progressive stages of oral precancerous conditions like oral submucous fibrosis (OSF). The classifier used is a three-layered feed-forward neural network and the feature vector, is formed by calculating the wavelet coefficients. Four wavelet decomposition functions, namely GABOR, HAAR, DB2 and DB4 have been used to extract the feature vector set and their performance has been compared. The samples used are transmission electron microscopic (TEM) images of collagen fibers from oral subepithelial region of normal and OSF patients. The trained network could classify normal fibers from less advanced and advanced stages of OSF successfully. (c) 2005 Elsevier Ltd. All rights reserved.
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| Publisher |
ELSEVIER SCIENCE BVAMSTERDAMPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
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| Date |
2011-09-20T12:12:06Z
2011-09-20T12:12:06Z 2006 |
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| Type |
Article
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| Identifier |
ORAL ONCOLOGY
1368-8375 http://hdl.handle.net/123456789/14120 |
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| Language |
English
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