Liver segmentation combining Gabor filtering and traditional vector field snake
Aaron M. Mintz, Daniela Stan Raicu, Jacob David Furst · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
This paper presents a study of a more accurately propagating deformable contour for outlining the liver in a Computed Tomography image of the abdomen, relying on the idea that a deformable parametric snake will propagate more accurately to the correct edges of an image when applied to textural information of the image as opposed to simple gray level information. The texture information is quantified using a set of Gabor filters and various methods of curve deformation are investigated, including a traditional vector field, gradient vector flow, and an expanding level-set method. Given the relative similarity in gray values of adjacent soft tissues, we found that a deformation algorithm that provides too large a capture range would be easily distracted by nearby values and therefore unsuitable for the particular task of segmenting the liver. Our results demonstrate both a general increase in performance of snake segmentation across the dataset as well as a significant regional improvement in accuracy, particularly in images corresponding with the top of the liver.