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Cephalometric Analysis using PCA and SVM

by M.Arulselvi, V. Ramalingam, S. Palanivel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 4
Year of Publication: 2011
Authors: M.Arulselvi, V. Ramalingam, S. Palanivel
10.5120/3628-5065

M.Arulselvi, V. Ramalingam, S. Palanivel . Cephalometric Analysis using PCA and SVM. International Journal of Computer Applications. 30, 4 ( September 2011), 39-47. DOI=10.5120/3628-5065

@article{ 10.5120/3628-5065,
author = { M.Arulselvi, V. Ramalingam, S. Palanivel },
title = { Cephalometric Analysis using PCA and SVM },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 4 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 39-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number4/3628-5065/ },
doi = { 10.5120/3628-5065 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:07.280096+05:30
%A M.Arulselvi
%A V. Ramalingam
%A S. Palanivel
%T Cephalometric Analysis using PCA and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 4
%P 39-47
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cephalometric analysis is the study of dental and skeletal relationship in the head. It depends on cephalometric radiography to study relationships between bony and soft tissue landmarks and can be used for diagnosis of facial growth abnormalities prior to treatment. Skeleton analysis consists of facial skeleton analysis, and mandibular and maxillary base analysis. In this work, landmarks needed for detecting skeletal abnormalities are selected from the digital image; Principal Component Analysis (PCA) is applied to the digital image for dimension reduction to get the desired feature vectors. The normalized feature vectors are trained and tested using an SVM classifier to detect the skeletal abnormalities. The performance measure such as accuracy, sensitivity and specificity were evaluated and the results are found to be satisfactory.

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Index Terms

Computer Science
Information Sciences

Keywords

Cephalometric analysis Principal component analysis Saddle Gonial Articular subspinale supramentale nasion sella

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