# A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow
## 5 Conclusions  
In this paper we described the implementation of a snake-based segmentation algorithm, based on an Active Contours Models (ACM), which has been integrated into a GUI based application. We focused our research in a method originally conceived for segmenting the shape of the heart's left ventricle. A modification of this specific prior has yielded very good results in the context of prostate cancer segmentation: the snake models tries to evolve and to adjust to the boundaries of the proposed zones. The problem with this implementation is that the performance of this previous model highly depends on the quality of the input image, as it uses the pixel intensities and the edges of the object in order to determine the correct curve evolution. As it can be expected, if the boundaries are not well defined, or if the intensities of different sections have similar values, then either the model will not be able to assess the difference, preventing the algorithm of evolving, or it will cause the snake to overlap with other elements in the image. This scenario can be observed in Figure 12, where the red contour overlaps with the area that corresponds to the TZ. Furthermore, in order to get that segmentation, several shapes for the initial contours had been tested to obtain the best results, which might be very different from one run of the algorithm to another. This entails that the implemented snake-based algorithms are very sensitive to the initialization conditions. This type of problems have fostered researches in image analysis and computer vision to find novel ways of constraining the shape of the force that pushes the objects' boundaries in the right direction. Doing so, the segmentation algorithm becomes robust against to initialization conditions and less sensitive to the quality of the image. Regarding the performance of both implementations, the _Simplified ACS_ and _Basic ACS_, their behaviour are not the same. Since _Basic ACS_ includes all the weights and all the functions described by the original document, the boundaries of the final segmentation tends to be closer to the real boundaries of the object to segment than in the case of _Simplified ACS_. Nonetheless, both of them fail in the task of finding the boundaries when the image intensities between two contiguous areas are not noticeably different, like in the case of the tumor and the central zone in Figure 12. This problem has been partially overcame when using the Gradient Vector Flow external force. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **CZ** & **PZ** & **TZ** & **TUM** \\ \hline
**Simplified ACS** & 605.79 & 231.69 & 317.19 & 616.93 \\
**Basic ACS** & 1054.97 & 574.30 & 819.18 & 1108.16 \\
**GVF ACS** & 641.66 & 644.95 & 725.72 & 1058.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution time (in ms) for each segmentation algorithm, and for each prostate zone. Further improvements to this algorithm will thus be directed towards implementing other types of Gradient Vector Flow energy optimization algorithms, or exploring how to include shape priors for each zone of the prostate separately. These modifications would help to create a segmentation that does not go too far from where the real object is. Additionally, no pre-processing nor post-processing functions have been included. Adding pre-processing operations might help to remove the noise that addresses the snake towards the wrong directions, and post-processing of the resulting contour will help to make it smoother, and closer to the real boundaries of the object. Moreover, since the DICOM files can contain different image modalities, and each modality has its particularities, a profiles section can be added to the program. Each profile will contain a set of parameters (priors weights, GVF weight, regularization constants, and iterations) specific for each algorithm. These profiles will be chosen in a way that they will produce the best segmentation results for the selected image modality, increasing the scope of application of the program.