Knowledge-based organ identification from CT images

This paper describes a new knowledge-based procedure for identifying and extracting organs from normal CT imagery. Our procedure differs from previous attempts in its use of a wide variety of knowledge about both the anatomy and the image processing operations. The system features the use of constraint-based dynamic thresholding, negative-shape constraints to rapidly rule out infeasible segmentations, and progressive landmarking that takes advantage of the different degrees of certainty of successful identification of each organ. The results of a series of tests on training data of 100 images from five patients plus additional test data of 75 images from three more patients indicate that the knowledge-based approach is promising. Knowledge-based vision Dynamic thresholding Medical imaging CT images