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&\#39; 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.
%0 Journal Article
%1 IJACSA.2012.030106
%A Aliaa A.A Youssif Abeer Saad Gawish, Mohammed Elsaid Moussa
%D 2012
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Biomedical classification. diseases epithelium extraction; feature image nuclear periodontal processing; segmentation;
%N 1
%T Automated Periodontal Diseases Classification System
%U http://ijacsa.thesai.org/
%V 3
%X 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&\#39; 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.
@article{IJACSA.2012.030106,
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\&\#39; 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.
},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Aliaa A.A Youssif Abeer Saad Gawish}, Mohammed Elsaid Moussa},
biburl = {https://www.bibsonomy.org/bibtex/25c97242d49a8630938cc41fb68e8e25f/thesaiorg},
interhash = {fea18a64503fe970e0e372930c01d5b0},
intrahash = {5c97242d49a8630938cc41fb68e8e25f},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Biomedical classification. diseases epithelium extraction; feature image nuclear periodontal processing; segmentation;},
number = 1,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Automated Periodontal Diseases Classification System}},
url = {http://ijacsa.thesai.org/},
volume = 3,
year = 2012
}