Patent ID: 8131038
Filing Date: 2012-03-06
Classification: G06K,G06T

Abstract:
1. A method for automatically segmenting a liver in digital medical images, comprising the steps of: providing a 3-dimensional (3D) digital image I to be segmented, said image comprising a plurality of intensities associated with a 3D grid of points; providing a set of N training shapes {φ selecting a seed point to initialize said segmentation; representing a level set function φ is a mean shape, {V i (x)} i=1, . . . , n are eigenmodes wherein n<N, α i are shape parameters corresponding to each training shape φ i , and h ε R 3 and θ ε [0,2π] 3 are translation and rotation parameters that align said training shapes; wherein H is a Heaviside function whose value is 1 inside said boundary and 0 otherwise, the log term is a shape prior distribution with standard deviation σ, and p in , p out are image intensity histogram functions inside and outside said boundary, respectively; minimizing said first energy functional to determine said shape, translation, and rotation parameters, wherein said shape, translation, and rotation parameters determine a shape template for said liver segmentation; defining a second energy functional of said shape template and a registration mapping weighted by image intensity histogram functions inside and outside said boundary; minimizing said second energy functional to determine said registration mapping, wherein said registration mapping recovers local deformations of said liver; and setting σ to twice an average Euclidean distance between each training shape and its closet neighbor in a subspace with dimension n.