Optimizing the Level Set Algorithm for Detecting Object Edges in MR and CT Images

Specifying the boundary of tissues or an organ is one of the most frequently required tasks for a radiologist. It is a first step for further processing, such as comparing two serial images in time, volume measurements. In the present work, we use genetic algorithms (GA) and where necessary apply a “dynamic genetic algorithm” (dGA) procedure, which (we believe) is a unique application, to assess different values for finding an optimal set of parameters that characterize the level set method, a geometric active contour, for use as a boundary detection method. Four quantitative measures are used in calculating geometric differences between the object boundaries, as determined by the level set method, and the desired object boundaries. A semi-automated method is also developed to find the desired boundary for the object. A two-step method requires the user to manipulate the object boundaries obtained by applying an edge detection method based on the Canny filter. By setting the level set method parameters using the output of the GA we obtain accurate boundaries of organs automatically and rapidly.