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Haghnegahdar, A A, Kolahi, S, Khojastepour, L, Tajeripour, F. (1396). Diagnosis of Tempromandibular Disorders Using Local Binary Patterns. سامانه مدیریت نشریات علمی, 8(1), 87-96.
A A Haghnegahdar; S Kolahi; L Khojastepour; F Tajeripour. "Diagnosis of Tempromandibular Disorders Using Local Binary Patterns". سامانه مدیریت نشریات علمی, 8, 1, 1396, 87-96.
Haghnegahdar, A A, Kolahi, S, Khojastepour, L, Tajeripour, F. (1396). 'Diagnosis of Tempromandibular Disorders Using Local Binary Patterns', سامانه مدیریت نشریات علمی, 8(1), pp. 87-96.
Haghnegahdar, A A, Kolahi, S, Khojastepour, L, Tajeripour, F. Diagnosis of Tempromandibular Disorders Using Local Binary Patterns. سامانه مدیریت نشریات علمی, 1396; 8(1): 87-96.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 8، شماره 1، خرداد 2018، صفحه 87-96    اصل مقاله (951.1 K)
نوع مقاله: Original Research
نویسندگان
A A Haghnegahdar1؛ S Kolahi* 1؛ L Khojastepour1؛ F Tajeripour2
1Department of Oral & Maxillofacial Radiology, school of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
2Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
چکیده
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.
کلیدواژه‌ها
Temporomandibular Joint Disorder؛ Cone-Beam Computed Tomography؛ Local Binary Pattern؛ Histogram of Oriented Gradients؛ K Nearest Neighbor
مراجع
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