A new effective and powerful medical image segmentation algorithm based on optimum path snakes

Abstract

Novel segmentation methods based on models of deformable active contours are constantly proposed and validated in different fields of knowledge, with the aim to make the detection of the regions of interest standard. This paper propose a new method called Optimum Path Snakes (OPS), a new adaptive algorithm and free of parameters to define the total energy of a active contour model with automatic initialization and stop criteria. In the experimental assessment, the OPS is compared against some approaches commonly used in the following fields, such as vector field convolution, gradient vector flow, and other specialists methods for lung segmentation using thorax computed tomography images. The segmentation of regions with stroke was carried out with methods based on region growing, watershed and a specialist level set approach. Statistical validations metrics using Dice coefficient (DC) and Hausdorff distance (HD) were also evaluated, as well as the processing time. The results showed that the OPS is a promising tool for image segmentation, presenting satisfactory results for DC and HD, and, many times, superior to the other algorithms it was compared with, including those generated by specialists. Another advantage of the OPS is that it is not restricted to specific types of images, neither applications.