Dongjin Kwon

Fast multiscale vessel enhancement filtering

This paper describes a fast multi-scale vessel enhancement filter in 3D medical images. For efficient review of the vascular information, clinicians need rendering the 3D vascular information as a 2D image. Generally, the maximum intensity projection (MIP) is a useful and widely used technique for producing a 2D image from the 3D vascular data. However, the MIP algorithm reduces the conspicuousness for small and faint vessels owing to the overlap of non-vascular structures. To overcome this invisibility, researchers have examined the multi-scale vessel enhancement filter based on a combination of the eigenvalues of the 3D Hessian matrix. This multi-scale vessel enhancement filter produces higher contrast. However, it is time-consuming and requires high cost computation due to large volume of data and complex 3D convolution. For fast vessel enhancement, we propose a novel multi-scale vessel enhancement filter using 3D integral images and 3D approximated Gaussian kernel. This approximated kernel looks like cube but it is not exact cube. Each layer of kernel is approximated 2D Gaussian second order derivative by dividing it into three rectangular regions whose sum is integer. 3D approximated kernel is a pile of these 2D box kernels which are normalized by Frobenius norm. Its size fits to vessel width in order to achieve better visualization of the small vessel. Proposed method is approximately five times faster and produces comparable results with previous multi-scale vessel enhancement filter.

Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering

In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence. A particle filter is employed as the main framework for tracking and is equipped with adaptive properties to both bone contact and vein contamination. The proposed tracking method is evaluated by the experiments on both synthetic and actual data. A variety of vessels were synthesized to assess the sensitivity to the axis curvature change, obscure boundaries, and noise. The experimental results showed that the proposed method is also insensitive to parameter settings and requires less user intervention than the conventional vessel tracking methods, which proves its improved robustness.

Nonrigid Image Registration Using Dynamic Higher-Order MRF Model

In this paper, we propose a nonrigid registration method using the Markov Random Field (MRF) model with a higher-order spatial prior. The registration is designed as finding a set of discrete displacement vectors on a deformable mesh, using the energy model defined by label sets relating to these vectors. This work provides two main ideas to improve the reliability and accuracy of the registration. First, we propose a new energy model which adopts a higher-order spatial prior for the smoothness cost. This model improves limitations of pairwise spatial priors which cannot fully incorporate the natural smoothness of deformations. Next we introduce a dynamicenergy model to generate optimal displacements. This model works iteratively with optimal data cost while the spatial prior preserve the smoothness cost of previous iteration. For optimization, we convert the proposed model to pairwise MRF model to apply the tree-reweighted message passing (TRW). Concerning the complexity, we apply the decomposedscheme to reduce the label dimension of the proposed model and incorporate the linear constrained node (LCN) technique for efficient message passings. In experiments, we demonstrate the competitive performance of the proposed model compared with previous models, presenting both quantitative and qualitative analysis.

Combining generative models for multifocal glioma segmentation and registration.

In this paper, we propose a new method for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas. Performing joint segmentation and registration for brain tumors is very challenging when tumors include multifocal masses and have complex shapes with heterogeneous textures. Our approach grows tumors for each mass from multiple seed points using a tumor growth model and modifies a normal atlas into one with tumors and edema using the combined results of grown tumors. We also generate a tumor shape prior via the random walk with restart, utilizing multiple tumor seeds as initial foreground information. We then incorporate this shape prior into an EM framework which estimates the mapping between the modified atlas and the scans, posteriors for each tissue labels, and the tumor growth model parameters. We apply our method to the BRATS 2013 leaderboard dataset to evaluate segmentation performance. Our method shows the best performance among all participants.

Optical flow estimation with adaptive convolution kernel prior on discrete framework

We present a new energy model for optical flow estimation on discrete MRF framework. The proposed model yields discrete analog to the prevailing model with diffusion tensor-based regularizer, which has been optimized by variational approach. Inspired from the fact that the regularization process works as a convolution kernel filtering, we formulate the difference between original flow and filtered flow as a smoothness prior. Then the discrete framework enables us to employ a robust penalizer less concerning convexity and differentiability of the energy function. In addition, we provide a new kernel design based on the bilateral filter, adaptively controlling intensity variance according to the local statistics. The proposed kernel simultaneously addresses over-segmentation and over-smoothing problems, which is hard to achieve by tuning parameters. Involving a complex graph structure with large label sets, this work also presents a strategy to efficiently reduce memory requirement and computational time to a tolerable state. Experimental result shows the proposed method yields plausible results on the various data sets including large displacement and textured region.

Fingerprint Matching Method Using Minutiae Clustering and Warping

Solving non-linear distortion problems in fingerprint matching is important and still remains as a challenging topic. We have developed a new fingerprint matching method to deal with non-linear distortion problems efficiently by clustering locally matched minutiae and warping the fingerprint surface using minutiae clusters. Specifically, local invariant structures encoding the neighborhood information of each minutia are utilized in clustering the matched minutiae and then the fingerprint surface is warped to describe the deformation pattern properly. Finally, to make an additional increase in performance, the overlapped region of two fingerprints is considered in the score computation stage. Experimental results show that the proposed algorithm is performed best compared with other ones

Deformable 3D Volume Registration Using Efficient MRFs Model with Decomposed Nodes

An efficient registration algorithm working on non-rigid 3D objects is presented. We formulate the registration as a discrete labe ling problem on MRFs model whose energy can be minimized by optimization techniques in the literature. Due to the huge search range in three-dimensional space, previous approaches produces a vast amount of labels for a node in the MRFs graph. To reduce the number of labels, we decompose a node into three nodes so that the labels in each node represent just one-dimensional displacement. This procedure introduces a factor node with a clique potential of size three, defining ternary interaction between the decomposed nodes. We convert the factor node into pairwise interactions and adopt the tree-r eweighted message passing technique, which guarantees the convergence of lower bound of the energy function. In experiments we use clinical and synthetically deformed 3D medical images. Result shows the proposed method enhances computational efficiency without loss of accuracy.

PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.

Regional Manifold Learning for Deformable Registration of Brain MR Images

We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order to confine the search space to anatomically plausible deformations. Existing methods construct manifolds based on a single metric over the entire image domain thus frequently miss regional brain variations. We address this issue by first learning manifolds for specific regions and then computing region-specific deformations from these manifolds. We then determine deformations for the entire image domain by learning the global manifold in such a way that it preserves the region-specific deformations. We evaluate the accuracy of our method by applying it to the LPBA40 dataset and measuring the overlap of the deformed segmentations. The result shows significant improvement in registration accuracy on cortex regions compared to other state of the art methods.

Efficient Feature-Based Nonrigid Registration of Multiphase Liver CT Volumes

This paper presents an efficient feature-based nonrigid registration method for multiphase liver CT volumes. While radiologists routinely examine multiphase liver CT to detect hepatic diseases, they usually search corresponding points between 3D CT volumes by visual inspections using 2D slice images. As the liver is a deformable organ, there exist complex nonrigid transformations between liver CT volumes obtained at difference time points (phases). We introduce a fully automatic registration application for multiphase liver CT volumes. For two given liver CT volumes, we extract 3D features with their descriptors, and estimate correspondences by finding nearest neighbor in descriptor space. An energy function is constructed using the correspondence information and the smoothness measure of free-form deformation model based on B-splines. We integrate an approximated smoothness energy function and a robust correspondence energy estimator controlled by the confidence radius of the matching distance in this energy model. The energy function is optimized by sequentially reducing the confidence radius, and outlier correspondences are discarded systematically during convergence. We propose a highly efficient optimization procedure using the preconditioned nonlinear conjugate gradient method. In the experiments, we will provide quantitative and qualitative results on synthetic and clinical data sets.