SEGMENTATION OF MEDICAL IMAGES

Segmentation and labelling remains the weakest step in many medical vision applications. This paper illustrates an approach based on generic modules which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters. Two of these modules are presented: the cavity detector, a method for the segmentation of regions which are not completely surrounded by walls and edgmentation, a modified split-and-merge algorithm for edge preserving image enhancement, segmentation and data reduction. We describe the principles of the algorithms and illustrate their generic properties by discussing the results of various applications in 2D and 3D cardiac MRI, in 3D and 4D cardiac SPECT, and in 2D brain X-ray CT.