Open AccessA Novel Approach towards Automatic Contour Identification of Jaw Cysts from Digital Panoramic Radiographs to improvise the Treatment planning
Author(s) -
Veena Divya K,
Anand Jatti,
M. J. Vidya,
Revan Kumar Joshi,
Srikar Gade
Publication year - 2022
Publication title -
international journal of biology and biomedical engineering
Language(s) - English
Resource type - Journals
ISSN - 1998-4510
DOI - 10.46300/91011.2022.16.1
Subject(s) - artificial intelligence , computer science , computer vision , segmentation , active contour model , radiography , process (computing) , image processing , identification (biology) , image segmentation , pattern recognition (psychology) , image (mathematics) , medicine , radiology , botany , biology , operating system
Panoramic dental x-ray, a two-dimensional dental x-ray that captures the entire mouth in a single image, is used for the initial screening of various dental anomalies. One such is Jaw bone cyst, which, if not identified earlier, may lead to complications which in turn may lead to disfigurement and loss of function. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region and extracting related features to assist clinical experts in further analysis. Objective: To develop an application of active contour model, known as Geodesic Active Contour, to generate Panoramic Dental X-Ray, a single 2 D X-ray image of the entire mouth highlighting the dental specifications. Methods: The process involves the image conversion from the OPG image into grayscale, Contrast adjustment using intensity level slicing, edge smoothing, segmentation, and cyst segmentation by Morphological Geodesic Active Contour to obtain the results. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region. It is crucial in extracting related features to assist clinical experts in further analysis. Conclusion: When efficient and accurate diagnostic methods exist, the treatment and cure become easy and concrete. Based on the morphological snake and level sets, it aims at identifying the boundary by minimizing the energy. Results: Using the structural similarity index, an accuracy of 97.6% is obtained. Advances in Knowledge: This process is advantageous as it is simpler, faster, and does not suffer from instability problems. Morphological methods improve their functional gradient descent by improving stability and speed. The hysteresis algorithm exhibits better edge detection performance, a significant reduction in computational time and scalability.