Compact rotation invariant descriptor for non-local means

Non-local means is a recently proposed denoising technique that better preserves image structures than other methods. However, the computational cost of non-local means is prohibitive, especially for large 3D images. Modifications have previously been proposed to reduce the cost, which result in image artefacts. This paper proposes a compact rotation invariant descriptor. Testing demonstrates improved denoising performance relative to optimized non-local means. Rotation invariant non-local means is an order of magnitude faster

Interactive image manipulation for surgical planning

The Australian e-Health Research Centre in collaboration with the Queensland University of Technology's Paediatric Spine Research Group is developing software for visualisation and manipulation of large three-dimensional (3D) medical image data sets. The software allows the extraction of anatomical data from individual patients for use in preoperative planning. State-of-the-art computer technology makes it possible to slice through the image dataset at any angle, or manipulate 3D representations of the data instantly. Although the software was initially developed to support planning for scoliosis surgery, it can be applied to any dataset whether obtained from computed tomography, magnetic resonance imaging or any other imaging modality

A new anisotropic diffusion method, application to partial volume effect reduction

The partial volume effect is a significant limitation in medical imaging that results in blurring when the boundary between two structures of interest falls in the middle of a voxel. A new anisotropic diffusion method allows one to create interpolated 3D images corrected for partial volume, without enhancement of noise. After a zero-order interpolation, we apply a modified version of the anisotropic diffusion approach, wherein the diffusion coefficient becomes negative for high gradient values. As a result, the new scheme restores edges between regions that have been blurred by partial voluming, but it acts as normal anisotropic diffusion in flat regions, where it reduces noise. We add constraints to stabilize the method and model partial volume; i.e., the sum of neighboring voxels must equal the signal in the original low resolution voxel and the signal in a voxel is kept within its neighbor's limits. The method performed well on a variety of synthetic images and MRI scans. No noticeable artifact was induced by interpolation with partial volume correction, and noise was much reduced in homogeneous regions. We validated the method using the BrainWeb project database. Partial volume effect was simulated and restored brain volumes compared to the original ones. Errors due to partial volume effect were reduced by 28% and 35% for the 5% and 0% noise cases, respectively. The method was applied to in vivo "thick" MRI carotid artery images for atherosclerosis detection. There was a remarkable increase in the delineation of the lumen of the carotid artery

Partial Volume Reduction by Interpolation with Reverse Diffusion

Many medical images suffer from the partial volume effect where a boundary between two structures of interest falls in the midst of a voxel giving a signal value that is a mixture of the two. We propose a method to restore the ideal boundary by splitting a voxel into subvoxels and reapportioning the signal into the subvoxels. Each voxel is divided by nearest neighbor interpolation. The gray level of each subvoxel is considered as “material” able to move between subvoxels but not between voxels. A partial differential equation is written to allow the material to flow towards the highest gradient direction, creating a “reverse” diffusion process. Flow is subject to constraints that tend to create step edges. Material is conserved in the process thereby conserving signal. The method proceeds until the flow decreases to a low value. To test the method, synthetic images were downsampled to simulate the partial volume artifact and restored. Corrected images were remarkably closer both visually and quantitatively to the original images than those obtained from common interpolation methods: on simulated data standard deviation of the errors were 3.8%, 6.6%, and 7.1% of the dynamic range for the proposed method, bicubic, and bilinear interpolation, respectively. The method was relatively insensitive to noise. On gray level, scanned text, MRI physical phantom, and brain images, restored images processed with the new method were visually much closer to high-resolution counterparts than those obtained with common interpolation methods

A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required to be performed. Unlike other learning-based registration, we propose a registration framework with inverse consistency. The proposed method simultaneously learns forward transformation and backward transformation in an unsupervised manner. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate strong performance than baseline registration methods

Half-mode substrate-integrated-waveguide cavity-backed slot antenna on cork substrate

A wideband half-mode substrate-integrated-waveguide cavity-backed slot antenna covering all Unlicensed National Information Infrastructure (U-NII) radio bands (5.15-5.85 GHz) is designed, fabricated, and validated. By a half-mode implementation of a multimoded cavity with nonresonant slot, a compact ultrawideband antenna is obtained with very stable radiation characteristics, owing to the excellent antenna/platform isolation. Cork material is applied as antenna substrate, making the proposed antenna suitable for integration into floors or walls. In free-space conditions, an impedance bandwidth of 1.30 GHz (23.7%), a radiation efficiency of 85%, a front-to-back ratio of 15.0 dB, and a maximum gain of 4.3 dBi at 5.50 GHz are measured. Performance is also validated when the antenna is deployed on various dielectric or conducting platforms and underneath different dielectric superstrates. Only the latter slightly detunes the antenna's impedance bandwidth. Yet, the complete frequency band of interest remains covered, owing to additional design margins incorporated in the requirements. Its compactness, unobtrusive integration potential, and stable high performance in different environments make this antenna topology an ideal candidate for Internet of Things applications

Pyramidal flux in an anisotropic diffusion scheme for enhancing structures in 3D images

Pyramid based methods in image processing provide a helpful framework for accelerating the propagation of information over large spatial domains, increasing the efficiency for large scale applications. Combined with an anisotropic diffusion scheme tailored to preserve the boundaries at a given level, an efficient way for enhancing large structures in 3D images is presented. In our approach, the partial differential equation defining the evolution of the intensity in the image is solved in an explicit scheme at multiple resolutions in an ascending-descending cycle. Intensity 'flux' between distant voxels is allowed, while preserving borders relative to the scale. Experiments have been performed both with phantoms and with real data from 3D Transrectal Ultrasound Imaging. The effectiveness of the method to remove speckle noise and to enhance large structures such as the prostate has been demonstrated. For instance, using two scales reduces the computation time by 87% as compared to a single scale. Furthermore, we show that the boundaries of the prostate are mainly preserved, by comparing with manually outlined edges

Consistent estimation of shape parameters in statistical shape model by symmetric EM algorithm

In order to fit an unseen surface using statistical shape model (SSM), a correspondence between the unseen surface and the model needs to be established, before the shape parameters can be estimated based on this correspondence. The correspondence and parameter estimation problem can be modeled probabilistically by a Gaussian mixture model (GMM), and solved by expectation-maximization iterative closest points (EM-ICP) algorithm. In this paper, we propose to exploit the linearity of the principal component analysis (PCA) based SSM, and estimate the parameters for the unseen shape surface under the EM-ICP framework. The symmetric data terms are devised to enforce the mutual consistency between the model reconstruction and the shape surface. The a priori shape information encoded in the SSM is also included as regularization. The estimation method is applied to the shape modeling of the hippocampus using a hippocampal SSM

A combined voxel and surface based method for topology correction of brain surfaces

Brain surfaces provide a reliable representation for cortical mapping. The construction of correct surfaces from magnetic resonance images (MRI) segmentation is a challenging task, especially when genus zero surfaces are required for further processing such as parameterization, partial inflation and registration. The generation of such surfaces has been approached either by correcting a binary image as part of the segmentation pipeline or by modifying the mesh representing the surface. During this task, the preservation of the structure may be compromised because of the convoluted nature of the brain and noisy/imperfect segmentations. In this paper, we propose a combined, voxel and surfacebased, topology correction method which preserves the structure of the brain while yielding genus zero surfaces. The topology of the binary segmentation is first corrected using a set of topology preserving operators applied sequentially. This results in a white matter/gray matter binary set with correct sulci delineation, homotopic to a filled sphere. Using the corrected segmentation, a marching cubes mesh is then generated and the tunnels and handles resulting from the meshing are finally removed with an algorithm based on the detection of nonseparating loops. The approach was validated using 20 young individuals MRI from the OASIS database, acquired at two different time-points. Reproducibility and robustness were evaluated using global and local criteria such as surface area, curvature and point to point distance. Results demonstrated the method capability to produce genus zero meshes while preserving geometry, two fundamental properties for reliable and accurate cortical mapping and further clinical studies

Atlas selection strategy in multi-atlas segmentation propagation with locally weighted voting using diversity-based MMR re-ranking

In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmentation accuracy converges slower comparing to simple voting rule. We therefore propose to introduce diversity in addition to image similarity by using Maximal Marginal Relevance (MMR) criteria as a more efficient way to rank and select atlases. We test the MMR re-ranking on a hippocampal atlas set of 138 normal control (NC) subjects and another set of 99 Alzheimer's disease patients provided by ADNI. The result shows that MMR re-ranking performed better than similarity based atlas selection when same number of atlases were selected