Multi-atlas segmentation method has attracted increasing attention in the field of medical image segmentation. It segments the target image by combining warped atlas labels according to a label fusion strategy, usually based on the intensity information of the target and atlas images. However, it has been demonstrated that image intensity information itself is not discriminative enough for distinguishing different subcortical structures in brain magnetic resonance (MR) images. Recent advance in multi-atlas based segmentation has witnessed success of label fusion methods built on informative image features. The key component in these methods is the image feature extraction. Conventional image feature extraction methods, such as textural feature extraction, are built on manually designed image filters and their performance varies when applied to different segmentation problems. In this paper, we propose a random local binary pattern (RLBP) method to generate image features in a random fashion. Based on RLBP features, we use a local learning strategy to fuse labels in multi-atlas based segmentation. Our method has been validated for segmenting hippocampus from MR images. The experiment results have demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods.
Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.
In imaging data based brain network analysis, a necessary precursor for constructing meaningful brain networks is to
identify functionally homogeneous regions of interest (ROIs) for defining network nodes. For parcellating the brain
based on resting state fMRI data, normalized cut is one widely used clustering algorithm which groups voxels according
to the similarity of functional signals. Due to low signal to noise ratio (SNR) of resting state fMRI signals, spatial
constraint is often applied to functional similarity measures to generate smooth parcellation. However, improper spatial
constraint might alter the intrinsic functional connectivity pattern, thus yielding biased parcellation results. To achieve
reliable and least biased parcellation of the brain, we propose an optimization method for the spatial constraint to
functional similarity measures in normalized cut based brain parcellation. Particularly, we first identify the space of all
possible spatial constraints that are able to generate smooth parcellation, then find the spatial constraint that leads to the
brain parcellation least biased from the intrinsic function pattern based parcellation, measured by the minimal Ncut value
calculated based on the functional similarity measure of original functional signals. The proposed method has been
applied to the parcellation of medial superior frontal cortex for 20 subjects based on their resting state fMRI data. The
experiment results indicate that our method can generate meaningful parcellation results, consistent with existing
functional anatomy knowledge.
In functional neuroimaging studies, the inter-subject alignment of functional magnetic resonance imaging (fMRI) data is
a necessary precursor to improve functional consistency across subjects. Traditional structural MRI based registration
methods cannot achieve accurate inter-subject functional consistency in that functional units are not necessarily
consistently located relative to anatomical structures due to functional variability across subjects. Although spatial
smoothing commonly used in fMRI data preprocessing can reduce the inter-subject functional variability, it may blur the
functional signals and thus lose the fine-grained information. In this paper we propose a novel functional signal based
fMRI image registration method which aligns local functional connectivity patterns of different subjects to improve the
inter-subject functional consistency. Particularly, the functional connectivity is measured using Pearson correlation. For
each voxel of an fMRI image, its functional connectivity to every voxel in its local spatial neighborhood, referred to as
its local functional connectivity pattern, is characterized by a rotation and shift invariant representation. Based on this
representation, the spatial registration of two fMRI images is achieved by minimizing the difference between their
corresponding voxels' local functional connectivity patterns using a deformable image registration model. Experiment
results based on simulated fMRI data have demonstrated that the proposed method is more robust and reliable than the
existing fMRI image registration methods, including maximizing functional correlations and minimizing difference of
global connectivity matrices across different subjects. Experiment results based on real resting-state fMRI data have
further demonstrated that the proposed fMRI registration method can statistically significantly improve functional
consistency across subjects.