Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Download Full-text

Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

BioMed Research International ◽

10.1155/2016/6727290 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10

Author(s):

Yunjie Chen ◽

Tianming Zhan ◽

Ji Zhang ◽

Hongyuan Wang

Keyword(s):

Magnetic Resonance ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Magnetic Resonance Images ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Proposed Model ◽

Brain Mr Images ◽

The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Download Full-text

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

NeuroImage ◽

10.1016/j.neuroimage.2018.08.003 ◽

2018 ◽

Vol 183 ◽

pp. 150-172 ◽

Cited By ~ 28

Author(s):

Aaron Carass ◽

Jennifer L. Cuzzocreo ◽

Shuo Han ◽

Carlos R. Hernandez-Castillo ◽

Paul E. Rasser ◽

...

Keyword(s):

Magnetic Resonance ◽

State Of The Art ◽

Magnetic Resonance Images

Download Full-text

A geometric flow-based approach for diffusion tensor image segmentation

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2008.0042 ◽

2008 ◽

Vol 366 (1874) ◽

pp. 2279-2292 ◽

Cited By ~ 4

Author(s):

Weihong Guo ◽

Yunmei Chen ◽

Qingguo Zeng

Keyword(s):

Magnetic Resonance ◽

Similarity Measure ◽

Diffusion Tensor ◽

Three Dimensional ◽

Positive Definite Matrix ◽

Magnetic Resonance Images ◽

Cerebral White Matter ◽

Geometric Flow ◽

Proposed Model ◽

Consistency Measure

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3×3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.

Download Full-text