# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 4 Results and Discussion
### Experimental Test  
After the pre-processing step (if necessary), users can select one of the segmentation algorithms at the same time. For each image, the segmented and masked images are generated and saved in a folder. In addition, all results are displayed in the application as a first preview. Often, there are long data repositories using the same acquisition system and the same protocol settings. If users can find a good procedure to segment a region of interest, it can be an excellent role to obtain many segmentation results for a long dataset. In addition, this configuration can be saved as a project file to be loaded for next time.  
One of the main advantages of this tool is that users can easily compare all the results of each segmentation technique. It is clear that not all segmentation techniques serve the same purpose. The idea is to offer different possibilities for users to select and choose the right technique according to the main objective. In the following, some results obtained using different images and segmentation techniques will be shown.  
Obviously, there are segmentation techniques that will not be useful depending on the image used. However, in general, with precision and using a dedicated study, excellent results have been obtained (Figure 6). In this case, a Watershed algorithm is used to segment regions in an ultrasound image. It is evident, that region growing or clustering techniques can be useful for this purpose. Threshold or active contour models can be adapted for other image modalities. Moreover, this example shows the total visualization of the GUI interface of MedicalSeg. In Figure 7 a GMM and snake algorithms are selected and compared to segment regions of mammographic images. All the results have been displayed to visualize and compare the obtained segmentations.  
On the other hand, Figure 8 is the example of image that is difficult to segment without a previous pre-defined region. There are a lot of structures that depending of the algorithm, the process could be tedious with bad results. In this example, edge detection (Canny automatic threshold) or Otsu method can be a good options if the idea is to segment the contours of the image. Snake or intensity algorithms are suitable alternatives as well.  
More examples are tested to segment regions of a magnetic resonance angiography of the brain (Figure 8). In this case, intensity segmentation is used to obtain the skeleton of the main structure of the arteries. Again, watershed is used to obtain sub-regions of the main structure, and GMM could be more precise in order to obtain the segmentation of these regions.  
Figure 6: Example of a ultrasound image using a Watershed algorithm.  
As a main purpose, the intention is to provide a tool useful to detect edges and use the generated masks to solve clinical problems, such as delimiting areas of interest, such as the prostate in perfusion images in MRI (Figure 8). Another example is to use a tool to segment the pectoral part of a mammogram in X-rays, or to quantify the dense or non-dense tissue of a breast with texture visualization (Figure 7). Moreover, another example using different techniques to know the clinical applicability of our approach applied to a slice image obtained from a TRANCE sequence is depicted in Figure 9. The interest could be conducted to find different regions of interest and clinical purposes, such as the presence of vascular brain malformations, circle of Willis, among others. This paper does not intend to give as much detail on the objectives that a radiologist or physician may have, but rather to demonstrate the usefulness of this tool for use in multiple disciplines.  
Figure 8: Example of a prostate MRI image using a Canny automatic threshold and Otsu algorithm.  
Figure 7: Example of a mammography MRI image using a GMM and Snake algorithm.