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.
