Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2012.030106
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 1, 2012.
Abstract: This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E) stained microscopic images. Our system uses image processing techniques based on color deconvolution, morphological operations, and watershed transforms for epithelium & connective tissue segmentation, nuclear segmentation, and extraction of the microscopic immunohistochemical features for the nuclei, dilated blood vessels & collagen fibers. Also, Feedforward Backpropagation Artificial Neural Networks are used for the classification process. We report 100% classification accuracy in correctly identifying the different periodontal diseases observed in our 30 samples dataset.
Aliaa A.A Youssif, Abeer Saad Gawish and Mohammed Elsaid Moussa, “Automated Periodontal Diseases Classification System” International Journal of Advanced Computer Science and Applications(IJACSA), 3(1), 2012. http://dx.doi.org/10.14569/IJACSA.2012.030106