0568366 20240402213615.820230210235959.9 10.2352/EI.2023.35.9.IPAS-300 5 s. E cav_un_epca*05683651017-9909SpringfieldSociety for Imaging Science and Technology2023 VKG Deep Learning Mucosal Wave Larynx Vocal Fold Videokymography Machine Learning cav_un_auth*0293261 Zita Aleš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0380013 Greško Šimon UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0283562 Novozámský Adam UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108377 Šorel Michal UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101238 Zitová Barbara UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0018322 Švec J. G. CZ cav_un_auth*0322230 Vydrová J. CZ http://library.utia.cas.cz/separaty/2023/ZOI/novozamsky-0568366.pdf TH04010422 GA TA ČR CZ cav_un_auth*0385138 GA21-03921S GA ČR cav_un_auth*0412209 Videokymographic (VKG) images of the human larynx are often used for automatic vibratory feature extraction for diagnostic purposes. One of the most challenging parameters to evaluate is the presence of mucosal waves and their lateral peaks' sharpness. Although these features can be clinically helpful and give an insight into the health and pliability of vocal fold mucosa, the identification and visual estimation of the sharpness can be challenging for human examiners and even more so for an automatic process. This work aims to create and validate a new method that can automatically quantify the lateral peak sharpness from the VKG images using a convolutional neural network. cav_un_auth*0445077 IS&T International Symposium on Electronic Imaging 2023 20230115 20230119 San Francisco US JC 20000 20200 20206 2024 7 PR RVO:67985556 https://hdl.handle.net/11104/0340752 cav_un_auth*0420485 Voice Research Laboratory, Department of Experimental Physics, Faculty of Science, Palacky University Olomouc CZ cav_un_auth*0357460 Medical Healthcom spol.s r.o. MH S 300 ENGINEERING.ELECTRICAL&ELECTRONIC|OPTICS|IMAGINGSCIENCE&PHOTOGRAPHICTECHNOLOGY 0.9 0.4 6.9 0.00172 0.168 2751 73 0.264 41.86 1.21 825 Q3 0.800 316 Q4 17.2 Q4 Q4 0.25 Q4 19.1 2023 SCOPUS PUBMED WOS cav_un_epca*0568365 IS&T International Symposium on Electronic Imaging 2023 Image Processing: Algorithms and Systems XXI 1017-9909 1560-229X Springfield Society for Imaging Science and Technology 2023 35