## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Glottal Gap Segmentation Techniques
#### 3.2.1 Image Enhancement  
Image enhancement refers to the manipulation or transformation of an image \(I^{R,G,B}(\mathbf{x},t)\), with the aim of increasing its usefulness or visual appearance. There are not general criteria behind the enhancement, and often the techniques used depend on the application [55]. In laryngeal images, the glottis has darker intensity levels than its surrounding tissues. However, they often have low contrast and heterogeneous profiles due to the illumination conditions. Modelling the histogram of HSV with a statistic distribution, such as Rayleigh as in [81], or finding the darkest region, produces errors due to the non-uniform contrast of the image, lighting conditions and artifacts due to the recording equipment. For this reason, it is required to simultaneously reduce the effect of the low contrast and to highlight the object of interest (i.e., the glottis). Thus, the use of image enhancing techniques is expected to improve the characteristics of the image for a further processing. The literature reports the use of different enhancing techniques as a prior step to glottis segmentation. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  
In [97], the authors combine an anisotropic diffusion with an FFT-based band pass filter in order to obtain a smoother image without losing edge information (second row of Figure 7). In [83] a Lagrange interpolation is combined with a Gaussian filter in order to smooth the images, reduce noise and eliminate unwanted details. In [85], the authors use a global thresholding to obtain a binary image to eliminate the worthless information. However, this strategy can not be generalized for noisy and poor quality HSV recordings. Another alternative is to manipulate the histogram of the image. The most common histogram based processing techniques are the Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited Histogram Equalization (CLHE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE). CLAHE is used in [90] providing more details in the glottal area while avoiding significant noise introduction (third row of Figure 7). CLAHE highlights the details over a small neighborhood preventing the over amplification of noise that can arise from adaptive histogram equalization AHE. In [91] the intensity of HSV is model as the product of an illumination function by a reflectance function. The illumination is estimated by temporally averaging the intensity for each pixel location in the glottis and the reflectance is obtained by dividing the image intensity into the illumination function. The reflectance distribution has a bimodal structure which is used by the authors to threshold HSV. One of the most widespread methods is based on point-wise intensity transformations. The point-wise transformation operates directly over the intensity values of an image, processing each pixel separately and independently. This transformation can be linear, piecewise linear, or nonlinear. Reference [99] establishes a methodology for pre-processing VS as a preliminary step for edge detection. The authors mention the drawbacks that exist in the acquisition due to the flashing effect at the recording instants, reducing the accuracy of the segmentation algorithm. The same procedure is used in [82] to highlight glottal area and to reduce influence of flashes in HSV. Considering the increasing use of deep learning techniques in medical image processing, an implementation of a Convolutional Neuronal Network (CNN) to enhance the low-light HSV is proposed in [106]. The training data include dark frames created by adding Perlin noise to the HSV and data augmentation to increase the number of videos to train. The quality of the laryngeal videos has a critical impact on an accurate diagnosis, examination, and identification of lesions or tissue changes [4]. Sequences with low quality are inadequate for quantitative analysis, reducing the amount of data available for research.