## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Glottal Gap Segmentation Techniques
#### 3.2.2 Region of Interest  
A ROI is a part of the image that encapsulates important features that can be used for further analysis. ROI detection has been studied for many years. Most algorithms use either feature-based or object-based approaches. Feature-based methods find pixels that share similar features to form the ROI. Meanwhile, object-based methods detect the ROI at a higher level than the pixel-by-pixel approach of feature-based systems using information such as target shape or structure. Figure 8 depicts some examples of ROI detection in six different HSV sequences using the algorithm presented originally in [107] and extended in [94].  
Figure 7: Visual comparison between three different enhancement methods. (**a**) original images extracted from three different videos; (**b**) enhancement method based on anisotropic with FFT-filter [97]; (**c**) enhancement method based on Contrast Limited Adaptive Histogram Equalization (CLAHE) [90]; (**d**) enhancement method based on nonlinear transformation [82].  
The literature devoted to solving the glottal gap segmentation reports attempts to detect a ROI. However, most of the studies require user intervention [75, 77, 78, 80, 81, 83, 85] and, even more important, few works take into account the temporal information of the sequence. Some approaches assume that a previous frame segmentation is available. Then, the pixels values are set to 1 when the difference between the current frame and the previous one is larger than a fixed threshold [82]. A similar strategy has been implemented in [72], which apply an algorithm based on differences between consecutive frames. Other works [11, 97, 98] use motion estimation techniques to compute the ROI based on the fact that the region with the most salient motion features corresponds to the vocal folds.  
In [90], an edge-based morphological approach is used to process some frames extracted from HSV, called keyframes. They search the object with the larger and nearly vertically oriented area by applying a Sobel filter. Lastly, a morphological closing operation is carried out over the gradient map to connect small related regions and the object with the largest area and vertical orientation is chosen. In [91, 94] a criterion based on the time change of the spatial intensity profile is used to detect the ROI for a fixed set of frames. The squared area to be tracked is selected adaptively based on the variations of the image intensity and the inter-frame disparity for an appropriate set of frames. By taking advantage of the continuous light source used in HSV, the area with the largest variability in time is identified as the glottis region. However, this approach presents drawbacks in cases as phonation onset, offset and total or partial paralysis of the vocal folds.  
In laryngeal images, the vocal folds, and so detected ROI, usually covers less than 25% of the entire image size. Therefore, the ROI detection permits to eliminate the non-relevant information and reduces the number of false detections, so it is an important step to be considered prior to the segmentation process.