Boundary extracting algorithm using region growing and snake model

Abstract

Boundary extracting and segmentation for ROI of medical image is an important prerequisite for analyzing, understanding and handling the images. Since snake model was proposed, it has been widely used at object contour detecting and tracking and the field of computer vision. In traditional algorithms, snake curve initialized manually was not accurate and the snake curve was easily attracted by the complex background, and its costing-time was so high. In order to overcome these shortcomings, this paper proposes a boundary extracting model based on region growing and snake model for medical images which have irregular region and complex features. Firstly, an improved adaptive region growing algorithm is used for boundary extracting approximately, then the region boundary is divided into four sub-boundaries, sample points in these boundaries, keep the points at large curvature position and balanceable between the sub-boundaries. Lastly, take these sampled points as the input of the contour searching and tracking in the snake model, and then improve and disperse inner and external energy function based on traditional snake model. The experimental results show that the new algorithm can detect the contour and deep boundary concavities of complex objects or malformed objects.