Automating cephalometric analysis in orthodontics using artificial neural network

Abstract

This study presented an Artificial Neural Network approach to promote Automate Cephelamatric Analysis in Orthodontics. Analysis and interpretation of standardized radiographs of the facial bones have become an important clinical task in Orthodontics. Conventional method of locating Landmarks depends on manual tracing ofthe radiographs. Since this is time consuming and error proven, demand for completely automate analysis and diagnostic tasks have increased. This study has critically reviewed four major problems in Cephelamatric Analysis; Precision of Landmark identification, Enormous time consumption, Subject to human errors and Need of continues support from experts. We argue that, issue of lack of autonomous solutions for Cephelamatric Analysis has been claimed to be the main problem with conventional approaches. There have been previous endeavors to Automate Cephalometric Analysis using Hand Crafted Algorithms, Mathematical or Statistical Models and Artificial Intelligence techniques. In any case accuracy was the same or worse than the one of manual identification. Therefore the aim of this investigation was to propose an Artificial Neural Network approach to computerize the Cephalometric Analysis. It is evident from the literature that, Neural Networks can introduce very high level of autonomy and accuracy in modeling real world problems. Therefore we hypothesized; Cephalometric Analysis can be automating by using self organizing feature of ANN. The proposed system automates Cephalometric Analysis along four dimensions. I.e. Image Acquisition using a Cephelostast and a scanner in order to capture the images and scan the images. Image Processing and Computer Vision to perform diffusion on gray scaled images and to detect possible edges using Canny. Two Landmarks, point-Me by finding the first existent edge ofthe image from RHs to LHS and edge starting from ‘Me’ is ended suddenly from the point -UIT , have identified and localized during this module. Coordinate along to the downward values of remaining extracted edges used as input to the ANN to detect other landmarks which cannot be identified directly during Computer Vision. Classify landmarks according to their geometrical specifications using a Competitive Neural Pinpoint the land marks according to the mean value of each cluster Network. obtained during ANN training. Users of the system are Orthodontists who will be benefitted from high level of accuracy and relatively fast outputs.