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Paper Information

Journal:   JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING   2018 , Volume 8 , Number 1; Page(s) 87 To 96.
 
Paper: 

DIAGNOSIS OF TEMPROMANDIBULAR DISORDERS USING LOCAL BINARY PATTERNS

 
 
Author(s):  HAGHNEGAHDAR A.A., KOLAHI S.*, KHOJASTEPOUR L., TAJERIPOUR F.
 
* DEPARTMENT OF ORAL & MAXILLOFACIAL RADIOLOGY, SCHOOL OF DENTISTRY, SHIRAZ UNIVERSITY OF MEDICAL SCIENCES, SHIRAZ, IRAN
 
Abstract: 

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.
Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients.
To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers.
We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis.
Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity.
Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

 
Keyword(s): TEMPOROMANDIBULAR JOINT DISORDER, CONE-BEAM COMPUTED TOMOGRAPHY, LOCAL BINARY PATTERN, HISTOGRAM OF ORIENTED GRADIENTS, K NEAREST NEIGHBOR
 
 
References: 
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Click to Cite.
APA: Copy

HAGHNEGAHDAR, A., & KOLAHI, S., & KHOJASTEPOUR, L., & TAJERIPOUR, F. (2018). DIAGNOSIS OF TEMPROMANDIBULAR DISORDERS USING LOCAL BINARY PATTERNS. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, 8(1), 87-96. https://www.sid.ir/en/journal/ViewPaper.aspx?id=570572



Vancouver: Copy

HAGHNEGAHDAR A.A., KOLAHI S., KHOJASTEPOUR L., TAJERIPOUR F.. DIAGNOSIS OF TEMPROMANDIBULAR DISORDERS USING LOCAL BINARY PATTERNS. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING. 2018 [cited 2021May09];8(1):87-96. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570572



IEEE: Copy

HAGHNEGAHDAR, A., KOLAHI, S., KHOJASTEPOUR, L., TAJERIPOUR, F., 2018. DIAGNOSIS OF TEMPROMANDIBULAR DISORDERS USING LOCAL BINARY PATTERNS. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, [online] 8(1), pp.87-96. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570572.



 
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