کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
3165833 | 1198854 | 2006 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition
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کلمات کلیدی
موضوعات مرتبط
علوم پزشکی و سلامت
پزشکی و دندانپزشکی
دندانپزشکی، جراحی دهان و پزشکی
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چکیده انگلیسی
SummaryThis 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Oral Oncology - Volume 42, Issue 9, October 2006, Pages 914–928
Journal: Oral Oncology - Volume 42, Issue 9, October 2006, Pages 914–928
نویسندگان
Anirban Mukherjee, Ranjan Rashmi Paul, Keya Chaudhuri, Jyotirmoy Chatterjee, Mousumi Pal, Provas Banerjee, Kanchan Mukherjee, Swapna Banerjee, Pranab K. Dutta,