this paper presents a proximity operator based GAC approach to MR image segmentation method. Our method
is a combination of geodesic active contours and the optimization tool of proximity operator in that it uses proximity operator
to iteratively deform the contour. Consequently, it has the following advantages. The operator has the ability to
jump over local minima and provide a more global result. The proximity operator is used to solve GAC fast image segmentation
model, gets an efficient iterative algorithm. Our approach easily extends to the segmentation of MRI objects
and not sensitive to the noise. In addition, the algorithm is suitable for interactive correction and is shown to always converge.
Experimental results and analyses are provided.