Multi-object and N-D segmentation of cardiac MSCT data using SVM classifiers and a connectivity algorithm

In this paper we present a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images. This method associates supervised classification methodologies with a region growing algorithm coupled with a connectivity approach. These concepts are combined in a competitive context implemented via a distributed real-time technology which allows multi-object detection. This approach is original by its atypical multi-object extraction, a rapidity of execution and the facility to introduce a priori information by the selection of a limited number of seed points inside the objects of interest. We apply this new method for the segmentation of cardiac structures observed in multislice computed tomography (MSCT) imaging. First results obtained on real 3D data reveals the good behaviour of the method, considering segmentation accuracy while minimizing user interaction and computational load.