Radial snakes: Comparison of segmentation methods in synthetic noisy images

There has been a growing use of digital image processing since the 90’s. To process an image it must be transformed successively in order to extract information more easily. The first steps in an image analysis are the acquisition followed by the preprocessing to prepare the image for the next step. This step is called image segmentation which is the process of separating different regions of the image according to their properties. The segmentation process is fundamental for all image analyses, as the final result is essentially dependent on the quality of the segmentation. Highlighted among these techniques are the active contour systems, known as snakes. The active contour methods can be subdivided in two main groups: two-dimensional search (traditional) and one-dimensional search (radial). The radial active contours were developed in order to obtain a smaller computational cost. The aim of this work was to study, evaluate and compare algorithms of radial active contours in synthetic noisy images and thus identify the advantages and disadvantages of each method in order to point out the most appropriate method for a given application. This work makes a quantitative and qualitative comparison of three methods: Traditional Radial Snakes, Hilbert Radial Snakes and pSnakes. The results of this research are suitable for academic research as they show that the recently developed pSnakes method is effective in image segmentation with noise. This paper also considered the processing time of the different methods.