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Automatic Segmentation of Glottal Space from Video Images Based on Mathematical Morphology and the hough Transform

机译:基于数学形态学和霍夫变换的视频图像声门空间自动分割

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Vocal disorders directly arise from the physical shape of the vocal cords. Videostroboscopic imaging provides doctors with valuable information about the physical shape of the vocal cords and about the way these cords move. Segmentation of the glottal space is necessary in order to characterize morphological disorders of vocal folds. One of the main problems with the methods presented is their low level of accuracy. To solve this problem, an automatic method based on Mathematical Morphology edge detection and the Hough transformation is presented in this article to extract the glottal space from the videostroboscopic images presented. Our method compared with the histogram and active contours methods and the findings showed that our proposed method yields better results.
机译:声带障碍直接源于声带的物理形状。频闪光谱成像为医生提供了有关声带的物理形状以及这些声带移动方式的有价值的信息。为了表征声带的形态障碍,必须对声门空间进行分割。提出的方法的主要问题之一是其准确性低。为了解决这个问题,本文提出了一种基于数学形态学边缘检测和霍夫变换的自动方法,从呈现的视频频闪图像中提取声门空间。我们的方法与直方图和主动轮廓法相比,结果表明我们的方法产生了更好的结果。

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