# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 4 Results and Discussion
### Performance Analysis
In this sense, Table 1 shows the results obtained against a comparison between the \(GT\) compared with the ground truth corresponding to MedicalSeg, and \(GT\) vs. ImageJ, respectively. Finally, a last comparison between MedicalSeg vs. ImageJ was also performed. As a main conclusion, the results obtained using ImageJ and MedicalSeg are quite similar compared with the ground truth and demonstrates the quality of the proposed segmentation approaches included in our application. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Dice Similarity Index (DSC)**} \\  & _GT_ **vs.** _MedicalSeg_ & _GT_ **vs.** _ImageJ_ & _MedicalSeg_ **vs.** _ImageJ_ \\ \hline Threshold & 0.96677 & 0.97550 & 0.99127 \\ Normalized & 0.97124 & 0.96998 & 0.99874 \\ Otsu & 0.99605 & 0.98256 & 0.98651 \\ Snake & 0.89595 & 0.91422 & 0.98713 \\ Canny auto & 0.16169 & 0.17881 & 0.98309 \\ Watershed & 0.032924 & 0.02749 & 0.99455 \\ GMM & 0.99600 & 0.92772 & 0.93172 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analyses the DSC for the ground truth (GT), MedicalSeg and ImageJ application for several segmentation techniques. In order to evaluate the performance of MedicalSeg a random case is used for each database. The criteria used to do the performance analysis is based on applying all the segmentation techniques for a group of images. For each set of images, the computation time (in seconds) for each segmentation technique is obtained. Moreover, the same set of images using all the techniques together is also performed. All the obtained results are depicted in Table 2. In this case, the parameters of each segmentation technique are fixed. In the previous section the results obtained for each experimental dataset applying all the segmentation techniques were discussed. In this section, the purpose is to obtain the elapsed time and the quantity of images generated using different experimental datasets. It is important to remark that in ThreshMAX percentage segmentation technique, the estimation of the elapsed time is performed by evaluating the method whithin a range of percentage options (40% to 80%). In the case of the snake segmentation algorithm the number of iterations was fixed to 30. Finally, Canny edge threshold for manual and automatic options were also computed. Analysing the results, the Canny edge detector and snake algorithms require more time than the others. It is normal because both algorithms require more steps and parameters that slow down the calculation iterative process. The purpose of this section is to estimate the computational time response according to the parameters presented in this work. Consequently, if users change these parameters the elapsed time will vary according the size of the dataset used for each test and the computing resources used. Finally, the size of the selected images from the dataset were 320 \(\times\) 240 cm, and the computer used to perform this test was an Intel(r) Core(tm) i7-6700 Processor 8M Cache, up to 4.00 GHz, 4 Cores (8 Threats) with 16 GB of RAM memory running in a Windows 10 OS.