Pre-Operative Tool for Facial-Post-Surgical Estimation and Detection
Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3299875Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 285
 American Society for Aesthetic Plastic Surgery, “Statistics 2018”. Available online at: https://www.plasticsurgery.org/news/plastic-surgery-statistics.
 Boundless Anatomy and Physiology in Structure of the Skin: Dermis. 2016. (Online). Available: https://www.boundless.com/physiology/textbookboundless-anatomy-and-physiology-textbook/ integumentary-system-5/theskin-64/structure-of-the-skin-dermis-395-7489/.
 CM Legemate et al., "Determining depth of burns using laser Doppler imaging", Nederlands tijschrift voor geneeskunde 162, 2018.
 Wearn C et al., "Prospective comparative evaluation study of laser Doppler Imaging and thermal imaging in the assessment of burn depth", Burns, 2018 Feb.
 Taiichiro Ida et al., "Real-Time photoacoustic imaging system for burn diagnosis", Journal of biomedical optics 19(8),086013, 2014.
 Taiichiro Ida et al., "Burn depth assessments by photoacoustic imaging and laser Doppler imaging", Wound repair and regeneration 24(2),349-355, 2016 March.
 Kittichai Wantanajittikul et al., "Automatic Segmentation and Degree Identification in Burn Color Images", BMEiCON-2011.
 Malini Suvarna, Sivakumar and U C Niranjan, "Classification Methods Of Skin Burn Images", IJCSIT, Vol. 5, No. 1 February 2013.
 Erwin Keeve, Sabine Girod, Paula Pfeifle, Bernd Girod. Anatomy Based Facial Tissue Modeling Using the Finite Element Method. IEEE Visualization 1996, 21-28.
 R. M. Koch, M. H. Gross, F. R. Carls, D. F. von Büren, G. Fankhauser, Y. I. H. Parish. Simulating facial surgery using finite element models. Proceedings of the SIGGRAPH’96. 1996. 421~428.
 Jie Liu et al., “A Novel Method for Computer Aided Plastic Surgery Prediction”, 2009 2nd International Conference Biomedical Engineering and Informatics.
 Klaudia Jamrozik et al., "Application Of Computer Modeling for planning Plastic Surgeries", MPER, vol.5, No.4 December 20.