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DOI: 10.14569/IJACSA.2012.030106
PDF

Automated Periodontal Diseases Classification System

Author 1: Aliaa A.A Youssif
Author 2: Abeer Saad Gawish
Author 3: Mohammed Elsaid Moussa

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 1, 2012.

  • Abstract and Keywords
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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.

Keywords: Biomedical image processing; epithelium segmentation; feature extraction; nuclear segmentation; periodontal diseases classification.

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

@article{Youssif2012,
title = {Automated Periodontal Diseases Classification System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.030106},
url = {http://dx.doi.org/10.14569/IJACSA.2012.030106},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {1},
author = {Aliaa A.A Youssif and Abeer Saad Gawish and Mohammed Elsaid Moussa}
}



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.

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