Diagnosis of Dental Deformities in Cephalometry Images Using Support Vector Machine

Abstract

This paper proposes an automated target recognition algorithm using Support Vector Machine (SVM) to extract landmark points for craniofacial features in cephalometry radiograph. The features are extracted by subjecting the radiograph to the Projected Principle Edge Distribution (PPED) algorithm. Edge flags are accumulated in every four columns and spatial distribution of edge flags are represented by a histogram. The resultants are the front end of support vector machine. Vectors, which possess land marks, are separated from all other vectors. The centroid points, automatically determined from PPED vectors, are the location of landmarks. The landmark points which are serving as a guide for construction and measurement of planes, are used to evaluate the dento-facial relationship, study of growth and development, and also for treatment planning.

Medical informatics research trend analysis: A text mining approach

“…Examples are bone disease classification using collected 3D image analysis and the artificial intelligence diagnosis of dental deformities in cephalometry images using a support vector machine. 53,54 Neural networks or computers are used to record and categorize patterns using the collected and recorded information, which in turn is the basis for data mining analysis and anomaly detections. Examples are electroencephalogram (EEG) recordings for a better description of sleep, automatic classification of long-term ambulatory electrocardiogram (ECG) records according to type of ischemic heart disease, automated detection of neonate EEG sleep stages, epileptic seizure detection in EEGs using time-frequency analysis, central sleep apnea detection from ECG-derived respiratory signals, a detection of Alzheimer’s disease using independent component analysis (ICA)-enhanced EEG measurements, and epileptic EEG signal detection using time-frequency distributions.…”

Diagnosis of periodontal diseases using different classification algorithms: A preliminary study

“…Support vector machine was previously used to diagnose dental deformities in cephalometry images and was found to be helpful in assisting dentists to quickly arrive at a conclusion whether a patient has been affected by any dental deformities or not. [13] The presented study is the first to use algorithmic systems for diagnosing periodontal diseases and also it is the first to compare the results of three different algorithms in predicting the diagnosis.…”

“…[7,8] ANNs, DT, and SVM were also compared with each other and performed for prediction models. [9] Although there are a few studies in dental area with the use of ANNs [10][11][12] and SVM, [13] there exist no available data for diagnosing and classification of periodontal diseases with the use of different types of algorithms alone or that compare the differences between each other.…”

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

“…Finally, angles between various landmark points are calculated to find out the deformities in the dento-facial growth. Banumathi A. et al [94] used in this study the Projected Principal Edge Distribution (PPED) vectors as a system for medical image recognition, and was used also in image recognition system dicussed above described by Tanikawa C et al [80], as this techniques proved to provide better results.…”

“…In 2011 Banumathi A et al suggested [94] Another diagnostic model, Artificial intelligence role in dentofacial deformities diagnosis was discussed. The dentist must be familiar with morphological and functional maturity also oral surgeon and the orthodontist should be able to relate this knowledge to specific clinical problems such as skeletal mealocclusion and craniofacial anomalies.…”