Robust 3D organ segmentation using a fast hybrid algorithm

Abstract This paper describes the use of a fast hybrid algorithm to segment organs from three-dimensional (3D) medical images. We first employ a recursive erosion operation to reduce the connectivity between the object to be segmented and its neighborhood; then the fast marching method is used to greatly accelerate the initial propagation of a surface front from the user defined seed structure to a surface close to the desired boundary; a morphological reconstruction method then operates on this surface to achieve an initial segmentation result; and finally recursive dilation is employed to recover any structure lost in the first stage of the algorithm. This approach is tested on 60 CT or MRI images of the brain, heart and urinary system, to demonstrate the robustness of this technique across a variety of imaging modalities and organ systems. The algorithm is also validated against datasets for which “truth” is known. These measurements revealed that the algorithm achieved a mean similarity index of 0.966 across the three organ systems. The execution time for this algorithm applying to three case studies was less than 50 s.