1. A method for lesion segmentation in 3-dimensional (3D) digital images comprising the steps of: providing a digitized 3D image, said image comprising a plurality of intensities associated with a 3D grid of voxels; selecting a 2D region of interest (ROI) from said 3D image, said ROI containing a suspected lesion, extending borders of said ROI to 3D forming a volume of interest (VOI), wherein voxels on said borders of said VOI are initialized as background voxels and voxels in an interior of said VOI are initialized as foreground voxels; propagating a foreground and background voxel competition wherein for each voxel in said VOI, having each neighbor voxel in a neighborhood of said voxel attack said voxel, and, if said attack is successful, updating a label and strength of said voxel with that of said successful attacking voxel; and evolving a surface between the foreground and background voxels in 3D until an energy functional associated with said surface converges in value, wherein said surface segments said suspected lesion from said image, wherein evolving said surface in 3D comprises: computing a surface evolution speed for each voxel along the zero level set according to ∂ S ∂ t = [ ( I - ∫ K i ⁢ ⁢ n ⁢ G ⁡ ( I ) ⁢ ⅆ Ω ∫ K i ⁢ ⁢ n ⁢ ⅆ Ω ) 2 - ( I - ∫ K out ⁢ G ⁡ ( I ) ⁢ ⅆ Ω ∫ K out ⁢ ⅆ Ω ) 2 + ακ ] ⁢ N , wherein S represents said surface, I is the image intensity function, Ω is the domain of the VOI in the image, G(I) is a Gaussian kernel with zero mean and a predetermined standard deviation, K in and K out are truncated domains of the Gaussian kernel inside and outside the surface, respectively, κ is a curvature function for smoothing the surface S, α is a weight of the smoothing, and N is a normal vector to the surface S; moving the surface by updating voxels representing said surface; and updating statistics of said Gaussian kernel and updating a narrowband about said updated surface.