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.
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