Albert Huang

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p<0.0001) and 10.18% (p<0.0001), respectively.

MRI Brain Extraction with Combined Expectation Maximization and Geodesic Active Contours

This paper presents a new fully automated method for the extraction of brain cortex from Tl-weighted magnetic resonance imaging (MRI) head scans. Combined with the expectation maximization (EM) algorithm, and a hybrid of pre- and post-processing techniques, incorporating mathematical morphology and connected component analysis, geodesic active contours are evolved in 3D space to segment the cortex. The robustness and accuracy of our proposed method are validated with both synthetic and real MRI data. Our method outperforms standard techniques including the brain extraction tool (BET) and statistical parametric mapping (SPM) by lowering the misclassification rate, especially when analyzing real MRI data

Automatic MRI brain tissue segmentation using a hybrid statistical and geometric model

This paper presents a novel hybrid segmentation technique incorporating a statistical as well as a geometric model in a unified segmentation scheme for brain tissue segmentation of magnetic resonance imaging (MRI) scans. We combine both voxel probability and image gradient and curvature information for segmenting gray matter (GM) and white matter (WM) tissues. Both qualitative and quantitative results on synthetic and real brain MRI scans indicate superior and consistent performance when compared with standard techniques such as SPM and FAST

A Fuzzy Region-Based Hidden Markov Model for Partial-Volume Classification in Brain MRI

We present a novel fuzzy region-based hidden Markov model (frbHMM) for unsupervised partial-volume classification in brain magnetic resonance images (MRIs). The primary contribution is an efficient graphical representation of 3D image data in which irregularly-shaped image regions have memberships to a number of classes rather than one discrete class. Our model groups voxels into regions for efficient processing, but also refines the region boundaries to the voxel level for optimal accuracy. This strategy is most effective in data where partial-volume effects due to resolution-limited image acquisition result in intensity ambiguities. Our frbHMM employs a forward-backward scheme for parameter estimation through iterative computation of region class likelihoods. We validate our proposed method on simulated and clinical brain MRIs of both normal and multiple sclerosis subjects. Quantitative results demonstrate the advantages of our fuzzy model over the discrete approach with significant improvements in classification accuracy (30% reduction in mean square error).

Image segmentation using an efficient rotationally invariant 3D region-based hidden Markov model

We present a novel three dimensional (3D) region-based hidden Markov model (rbHMM) for unsupervised image segmentation. Our contributions are twofold. First, our rbHMM employs a more efficient representation of the image than approaches based on a rectangular lattice or grid; thus, resulting in a faster optimization process. Second, our proposed novel tree-structured parameter estimation algorithm for the rbHMM provides a locally optimal data labeling that is invariant to object rotation. We demonstrate the advantages of our segmentation technique by validating on synthetic images of geometric shapes as well as both simulated and clinical magnetic resonance imaging (MRI) data of the brain. For the geometric shape data, we show that our method produces more accurate results in less time than a grid-based HMM framework using a similar optimization strategy. For the brain MRI data, our white and gray matter segmentation results in substantially greater accuracy than both block-based 3D HMM estimation and expectation-maximization hidden Markov random field (HMRF-EM) approaches.

A Novel Rotationally Invariant Region-Based Hidden Markov Model for Efficient 3-D Image Segmentation

We present a novel 3-D region-based hidden Markov model (rbHMM) for efficient unsupervised 3-D image segmentation. Our contribution is twofold. First, rbHMM employs a more efficient representation of the image data than current state-of-the-art HMM-based approaches that are based on either voxels or rectangular lattices/grids, thus resulting in a faster optimization process. Second, our proposed novel tree-structured parameter estimation algorithm for the rbHMM provides a locally optimal data labeling that is invariant to object rotation, which is a highly valuable property in segmentation tasks, especially in medical imaging where the segmentation results need to be independent of patient positioning in scanners in order to minimize methodological variability in data analysis. We demonstrate the advantages of our proposed technique over grid-based HMMs by validating on synthetic images of geometric shapes as well as both simulated and clinical brain MRI scans. For the geometric shapes data, our method produced consistently accurate segmentation results that were also invariant to object rotation. For the brain MRI data, our white matter and gray matter segmentation resulted in substantially higher robustness and accuracy levels with improved Dice similarity indices of 4.60% (p=0.0022) and 7.71% (p<;0.0001) , respectively.