Criteria of loop iteration break for level-set-based medical image segmentation

Abstract

Medical images have the characteristics of high noise and blurred edges, which makes them difficult to segment using traditional segmentation methods. The level set algorithm, which is a commonly used method for medical image segmentation, is restricted in use mainly due to the extremely intensive computation during the iterative contour evolution. The paper proposes some criteria of loop iteration break for the level set algorithm, making it possible to adaptively adjust the number of iterations to the specific characteristics of various medical images, so that the contour evolution can be terminated appropriately. Meanwhile, we change the step length of the iteration according to the previous loop iteration result, making it possible to decrease the number of iterations needed. To decrease the computational workload, we also restrict the iteration to a certain part of the image instead of the whole image.