An Automated Approach to the Initialization of the Snakes Algorithm for the Detection of Swimbladder Regions in X-Ray Image Data.

Snakes have been widely used for object tracking, shape detection, and segmentation of an area of interest within image data. A snakes algorithm uses an energy minimization approach to deform an initial boundary or curve so that it traces along the contour of a shape in an image. However, a major disadvantage of the algorithm is that it requires users to draw the initial boundary in the image of an object, which is not feasible when large number of images need to be processed or when user-introduced bias in the selection of the initial boundary may influence the accurate detection of objects. This paper reports on an algorithm for the automatic detection of a region of interest that utilizes a snakes algorithm for image segmentation. It specifically combines multiple image processing and screening techniques to build a pipeline of processing steps that produces the initial boundary of a region in an image for the initialization of a snakes algorithm. The approach has been evaluated on X-ray images of the striped burrfish to detect the swimbladder as a region of interest. Results from the fully automated algorithm are compared against ground truth values and semi-automated algorithm results.