Zdimalova M, Roznovjak R, Weismann P, El Falougy H, Kubikova E
Bratisl Lek Listy. 2017;118(8):485-490. doi: 10.4149/BLL_2017_093.
Image segmentation is a known problem in the field of image processing. A great number of methods based on different approaches to this issue was created. One of these approaches utilizes the findings of the graph theory.
Our work focuses on segmentation using shortest paths in a graph. Specifically, we deal with methods of "Intelligent Scissors," which use Dijkstra's algorithm to find the shortest paths.
We created a new software in Microsoft Visual Studio 2013 integrated development environment Visual C++ in the language C++/CLI. We created a format application with a graphical users development environment for system Windows, with using the platform .Net (version 4.5). The program was used for handling and processing the original medical data.
The major disadvantage of the method of "Intelligent Scissors" is the computational time length of Dijkstra's algorithm. However, after the implementation of a more efficient priority queue, this problem could be alleviated. The main advantage of this method we see in training that enables to adapt to a particular kind of edge, which we need to segment. The user involvement has a significant influence on the process of segmentation, which enormously aids to achieve high-quality results (Fig. 7, Ref. 13).
图像分割是图像处理领域一个已知的问题。针对这个问题，人们创建了大量基于不同方法的算法。其中一种方法利用了图论的研究成果。
我们的工作重点是利用图中的最短路径进行分割。具体来说，我们研究了“智能剪刀”方法，该方法使用迪杰斯特拉算法来寻找最短路径。
我们在Microsoft Visual Studio 2013集成开发环境中，使用C++/CLI语言在Visual C++中创建了一个新软件。我们使用.Net（版本4.5）平台，为Windows系统创建了一个带有图形用户开发环境的格式化应用程序。该程序用于处理和加工原始医学数据。
“智能剪刀”方法的主要缺点是迪杰斯特拉算法的计算时间较长。然而，在实现了更高效的优先级队列后，这个问题可以得到缓解。我们认为这种方法的主要优点在于其训练过程能够适应我们需要分割的特定类型的边缘。用户的参与对分割过程有重大影响，这极大地有助于获得高质量的结果（图7，参考文献13）。
