## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Glottal Gap Segmentation Techniques
#### 3.2.3 Glottal Gap Delimitation  
The image segmentation methods can be classified, roughly speaking, into the following categories: thresholding, edge-based, region-based, classification-based, graph-based and deformable models. With respect to the glottal gap delimitation, the most common approaches are based on thresholding, region growing and deformable models (parametric and geometric).  
The studies based on thresholding assume that the glottis has darker intensity levels than the vocal fold tissues [71, 81]. However, the laryngeal images often have low contrast and heterogeneous  
Figure 8: Automatic ROI detection of six different HSV computed based on [94, 107]. The image in (f) illustrates a minor problem in the detection of the ROI in the posterior commissure.  
profiles. Hence, selecting a global threshold results in an erroneous delimitation of the glottal gap, since the intensity distribution is not bimodal. The studies based on region growing require a solid criterion for the seed selection and relatively well-delimited edges in order to converge towards the glottal gap. Furthermore, the algorithms segment objects with inhomogeneous regions into multiple sub-regions, resulting in over-segmentation [73; 74]. With respect to the deformable models, they have the advantage to couple appropriately to non-rigid and amorphous contours by an iterative minimization of an energy function. However, the initialization process is not a trivial task. Therefore, many authors use manual procedures to initialize the active contours [77; 78; 80].  
There are other approaches that combine different image-processing techniques [75; 86; 89; 91; 94; 97], use feature extraction and training [95], or make use of more recent paradigms such as Deep Neural Networks [96]. For instance, [89] present an automatic glottis segmentation approach using deformable shape models and region growing. The approach starts with an initial coarse segmentation by means of the Region Growing technique. The seed points are determined based on a simple linear relationship between the average gray level of the image and the optimal seed points obtained from the training examples. Lastly, the non-glottal regions are eliminated using the reliability score factor from the trained shape models.  
In [86] the textures of the background (laryngeal structures) and foreground (glottal gap) are smoothing. Then, the glottis is detected based on the temporal intensity variation of laryngeal videos and segmented using a background subtraction method. In [95], a fully automatic method is used to segment the glottis using local color and shape information. The approach is divided into three modules: training, recognition and segmentation. In the training, 60 different glottis shapes are manually segmented, and a set of descriptors are computed. The recognition module is designed to recognize, delineate and determine the optimal starting glottis regions. The last module segments the glottis based on properties of the previous frame. Hence, the glottis is continuously tracked within vibration cycles of the video by a frame-by-frame-wise segmentation technique.  
In [96] a Deep Neural Network is implemented to segment the glottal gap in VS. The glottis color structure and its neighboring color pattern are employed to create the features vectors. Then, these vectors are used to train the network and classify whether each pixel belongs to the glottal region or not.  
One of the most extended methods to perform glottal gap segmentation is presented in [73]. The advantage of the method lies in its semi-automatic characteristic since the wrong detection of the glottal gap can be fixed during the segmentation process. An open-source version of this algorithm called \"GlottalImageExplorer\" is available in [108]. With a friendly interface, the tool lets the user enter three threshold points, which define the threshold progression for the seeded region growing, and the threshold progression is defined by the linear interpolation between the threshold points.