Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video

Kuo, Chung‐Feng Jeffrey; Chu, Yueng‐Hsiang; Wang, Po-Chun; Lai, Chun-Yu; Chu, Wen-Lin; Leu, Yi-Shing; Wang, Hsing-Won

doi:10.1016/j.cmpb.2013.08.005

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Many medical screenings include informative frame selection, such as image screening for laryngeal disease detection and redundant image elimination. These are not imited to laryngoscopy [ 17 , 25 ], e.g., gastrointestinal endoscopy [ 26 ], wireless capsule endoscopy (WCE) [ 27 , 28 ], optical endomicroscopy [ 29 ], and colonoscopy [ 30 ]. We have grouped these methods into two categories.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

Zhang

Wei

et al. 2023

Diagnostics

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Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling.

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Section: Related Workmentioning

confidence: 99%

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

Zhang

Wei

et al. 2023

Diagnostics

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“…Vocal fold vibration is a highly complicated, compact three-dimensional vibration. The observation and measurement of vocal fold vibrations using equipment such as electroglottographs (EGGs) [1,2,3], video laryngoscopes [4], and high-speed video devices [5,6,7] have been successfully applied for studying the motion of the vocal cord tissues. However, these methods cannot directly express the vocal cord motion characteristics and they must be applied to the throat, causing discomfort to patients.…”

Section: Introductionmentioning

confidence: 99%

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

Chen

Sheng

Zhang

et al. 2017

Sensors

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The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance.

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“…Voigt et al 17 studied the clinical diagnosis of voice disorders by examining the rapidly moving vocal folds during phonation with an endoscopic high-speed camera. Kuo et al 18 designed an automatic vocal cord image selection system to improve the conventional selection process by physicians and enhance diagnosis efficiency. Kuo et al 19 implemented an automatic vocal fold disease identification system, which could obtain the physiological parameters for normal vocal folds, vocal paralysis, and vocal nodules from image processing according to the pathological features.…”

Section: Introductionmentioning

confidence: 99%

Automatic and quantitative measurement of laryngeal video stroboscopic images

Kuo

Hsiao

et al. 2016

Proc Inst Mech Eng H

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The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest. Information of the glottis was quantified, and the vocal fold image segmentation system was completed to assist clinical diagnosis and increase accuracy. Regarding image processing, histogram equalization was used to enhance glottis image contrast. The center weighted median filters image noise while retaining the texture of the glottal image. Statistical threshold determination was used for automatic segmentation of a glottal image. As the glottis image contains saliva and light spots, which are classified as the noise of the image, noise was eliminated by erosion, expansion, disconnection, and closure techniques to highlight the vocal area. We also used image processing to automatically identify an image of vocal fold region in order to quantify information from the glottal image, such as glottal area, vocal fold perimeter, vocal fold length, glottal width, and vocal fold angle. The quantized glottis image database was created to assist physicians in diagnosing glottis diseases more objectively.

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Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video

Cited by 10 publications

References 12 publications

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

Automatic and quantitative measurement of laryngeal video stroboscopic images

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