Medical Image Segmentation Using Active Contours and a Level Set Model: Application to Pulmonary Embolism (PE) Segmentation

Level set methods are a powerful numerical technique for image segmentation, analysis and extracting boundary curves. This method requires the definition of a speed function that controls curve evolution. The image intensity gradient and the curvature are utilized together to determine the speed and direction of the propagation. Although level set methods are highly effective in segmenting image, but. they are sometimes unable to exactly detect objects in images with low-contrast boundaries. In this paper we use hybrid speed functions for an implicit active contour (level set) method which is capable of segmenting images with low-contrast boundaries. These functions are formulated based on both image intensity and image gradient. In this work, we use the proposed speed function to detect the pulmonary artery (PA) based on level set method in CTA images where edges may be weak. Finally, we detect the pulmonary embolism (PE) with a thresholding method within the pulmonary artery (PA).