A new label fusion method using graph cuts: application to hippocampus segmentation
نویسندگان
چکیده
The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains. Keywords— Label fusion, atlas-based segmentation, hippocampus segmentation
منابع مشابه
Segmentation of Subcortical Structures in Brain Mri Using Graph-cuts and Subject-specific A-priori Information
We propose a general framework for segmentation of subcortical structures in magnetic resonance brain images based on multi-atlas label propagation and graph cuts. The label maps obtained from multi-atlas segmentation are used to build a subject-specific probabilistic atlas of a structure of interest. From this atlas and an intensity model estimated from the unseen image, a Markov random field-...
متن کاملPatch-Based Label Fusion with Spatio-Temporal Graph Cuts for Cardiac MR Images
A patch-based method is proposed for cardiac MR image sequence segmentation, combined with the graph cuts algorithm to guarantee spatio-temporal smoothness of the segmentation. It was tested on the challenge training set with 83 subjects and achieved an average Dice metric of 0.792 for the myocardium.
متن کاملA comparative performance of gray level image thresholding using normalized graph cut based standard S membership function
In this research paper, we use a normalized graph cut measure as a thresholding principle to separate an object from the background based on the standard S membership function. The implementation of the proposed algorithm known as fuzzy normalized graph cut method. This proposed algorithm compared with the fuzzy entropy method [25], Kittler [11], Rosin [21], Sauvola [23] and Wolf [33] method. M...
متن کاملAutomated Cerebellar Lobule Segmentation using Graph Cuts
The cerebellum is important in coordinating many vital functions including speech, motion, and eye movement. Accurate delineation of sub-regions of the cerebellum, into cerebellar lobules, is needed for studying the region specific decline in function from cerebellar pathology. In this work, we present an automated cerebellar lobule segmentation method using graph cuts, with a region-based term...
متن کاملMulti-Label MRF Optimization via Least Squares s-t Cuts
There are many applications of graph cuts in computer vision, e.g. segmentation. We present a novel method to reformulate the NP-hard, k-way graph partitioning problem as an approximate minimal s− t graph cut problem, for which a globally optimal solution is found in polynomial time. Each non-terminal vertex in the original graph is replaced by a set of ceil(log2(k)) new vertices. The original ...
متن کامل