%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 }