Technology and health care : official journal of the European Society for Engineering and Medicine · 2015
Vocal folds morphological pathologies detection using Gabor filtering and Principal Component Analysis.
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Mendez-Zorrilla A, Garcia-Zapirain B
Аннотация
BACKGROUND: Given the importance of the voice in our daily lives, any study focused on its pathologies and the way of caring and promoting the health of them is of common interest.
OBJECTIVE: This paper describes a method to automatically aid indetecting vocal folds benign pathologies based on glottal space segmentation vocal fold video sequences captured by a laryngoscope.
METHODS: The proposed algorithm is based on automatic segmentation supported by Gabor filters, and features classification with Principal Component Analysis (PCA) to achieve the expected results.
RESULTS: The authors wish to emphasize that the proposed algorithm is independent from the images' resolution and zoom, but their quality depends on specialist experience with the instrumentation. Segmentation block provides good results for 95% of images and classification block distinguishes successfully between pathological and healthy images in 92.1% of cases. The proposed system's findings have been compared with the diagnosis made by doctors and it obtains the same results in all the 45 sequences.
CONCLUSIONS: One of the proposed study's key elements has been which objective measurements are of significance for the specialist. In this case, it is those that enable the specialist to calculate the size of the pathology (previously classified automatically) that he/she is observing, thus enabling them to provide the patient with more information or to prescribe treatment and even measure its development.
OBJECTIVE: This paper describes a method to automatically aid indetecting vocal folds benign pathologies based on glottal space segmentation vocal fold video sequences captured by a laryngoscope.
METHODS: The proposed algorithm is based on automatic segmentation supported by Gabor filters, and features classification with Principal Component Analysis (PCA) to achieve the expected results.
RESULTS: The authors wish to emphasize that the proposed algorithm is independent from the images' resolution and zoom, but their quality depends on specialist experience with the instrumentation. Segmentation block provides good results for 95% of images and classification block distinguishes successfully between pathological and healthy images in 92.1% of cases. The proposed system's findings have been compared with the diagnosis made by doctors and it obtains the same results in all the 45 sequences.
CONCLUSIONS: One of the proposed study's key elements has been which objective measurements are of significance for the specialist. In this case, it is those that enable the specialist to calculate the size of the pathology (previously classified automatically) that he/she is observing, thus enabling them to provide the patient with more information or to prescribe treatment and even measure its development.