A Verikas1, A Gelzinis, M Bacauskiene, V Uloza. 1. Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania. antanas.verikas@ide.hh.se
Abstract
OBJECTIVE: The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. METHODOLOGY: The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes. RESULTS: The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes. CONCLUSION: Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.
OBJECTIVE: The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. METHODOLOGY: The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes. RESULTS: The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes. CONCLUSION: Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.
Authors: Nikita V Orlov; Wayne W Chen; David Mark Eckley; Tomasz J Macura; Lior Shamir; Elaine S Jaffe; Ilya G Goldberg Journal: IEEE Trans Inf Technol Biomed Date: 2010-07
Authors: Daniel R Witt; Huijun Chen; Jason D Mielens; Kieran E McAvoy; Fan Zhang; Matthew R Hoffman; Jack J Jiang Journal: J Voice Date: 2013-12-05 Impact factor: 2.009