A multiresolution binary level set method and its application to intracranial hematoma segmentation

Abstract 

We propose a multiresolution binary level set method for image segmentation. The binary level set formulation is based on the Song–Chan algorithm, which cannot compute the edge length when the margin of the image is irregular. We modify the edge length approximation so that it can work everywhere in a single-connected image, make it suitable to segment objects at any position, especially near the margin of the image. For multiresolution processing, we use image pyramids. The binary level set method works on images with reduced resolution and size. A point at the image with lower resolution is processed instead of a block or a strip at the original resolution, therefore improving the efficiency. Our multiresolution binary level set method is applied to segmentation of intracranial hematomas on brain CT slices. Segmentation of epidural and subdural hematomas, which have been not done previously, is performed successfully in seconds with results comparable to human experts.

Keywords: Image segmentation, Level set method, Intracranial hematoma, Computed tomography, Brain deformation, Pathological images, Decision support system