Superpixel-based segmentation of glottal area from videolaryngoscopy images

H. Irem Turkmen; Abdulkadir Albayrak; M. Elif Karsligil; Ismail Kocak

doi:10.1117/1.JEI.26.6.061608

26 September 2017 Superpixel-based segmentation of glottal area from videolaryngoscopy images

H. Irem Turkmen, Abdulkadir Albayrak, M. Elif Karsligil, Ismail Kocak

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 061608 (September 2017). https://doi.org/10.1117/1.JEI.26.6.061608

Abstract

Segmentation of the glottal area with high accuracy is one of the major challenges for the development of systems for computer-aided diagnosis of vocal-fold disorders. We propose a hybrid model combining conventional methods with a superpixel-based segmentation approach. We first employed a superpixel algorithm to reveal the glottal area by eliminating the local variances of pixels caused by bleedings, blood vessels, and light reflections from mucosa. Then, the glottal area was detected by exploiting a seeded region-growing algorithm in a fully automatic manner. The experiments were conducted on videolaryngoscopy images obtained from both patients having pathologic vocal folds as well as healthy subjects. Finally, the proposed hybrid approach was compared with conventional region-growing and active-contour model-based glottal area segmentation algorithms. The performance of the proposed method was evaluated in terms of segmentation accuracy and elapsed time. The F-measure, true negative rate, and dice coefficients of the hybrid method were calculated as 82%, 93%, and 82%, respectively, which are superior to the state-of-art glottal-area segmentation methods. The proposed hybrid model achieved high success rates and robustness, making it suitable for developing a computer-aided diagnosis system that can be used in clinical routines.

Citation Download Citation

H. Irem Turkmen, Abdulkadir Albayrak, M. Elif Karsligil, and Ismail Kocak "Superpixel-based segmentation of glottal area from videolaryngoscopy images," Journal of Electronic Imaging 26(6), 061608 (26 September 2017). https://doi.org/10.1117/1.JEI.26.6.061608

Received: 25 April 2017; Accepted: 29 August 2017; Published: 26 September 2017

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