Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correlation

Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection has become an important branch of remote–sensing image processing due to its important theoretical value and wide practical application prospects. However, some anomaly detection methods mainly exploit the spectral feature and do not make full use of spatial features, thus limiting the performance improvement of anomaly detection methods. Here, a novel hyperspectral anomaly detection method, called spectral–spatial complementary decision fusion, is proposed, which combines the spectral and spatial features of a hyperspectral image (HSI). In the spectral dimension, the three–dimensional Hessian matrix was first utilized to obtain three–directional feature images, in which the background pixels of the HSI were suppressed. Then, to more accurately separate the sparse matrix containing the anomaly targets in the three–directional feature images, low–rank and sparse matrix decomposition (LRSMD) with truncated nuclear norm (TNN) was adopted to obtain the sparse matrix. After that, the rough detection map was obtained from the sparse matrix through finding the Mahalanobis distance. In the spatial dimension, two–dimensional attribute filtering was employed to extract the spatial feature of HSI with a smooth background. The spatial weight image was subsequently obtained by fusing the spatial feature image. Finally, to combine the complementary advantages of each dimension, the final detection result was obtained by fusing all rough detection maps and the spatial weighting map. In the experiments, one synthetic dataset and three real–world datasets were used. The visual detection results, the three–dimensional receiver operating characteristic (3D ROC) curve, the corresponding two–dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) were utilized as evaluation indicators. Compared with nine state–of–the–art alternative methods, the experimental results demonstrate that the proposed method can achieve effective and excellent anomaly detection results.

Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

MR venography of the brain with enhanced vessel contrast using image-domain high-pass filtering of the susceptibility phase shift

PURPOSE

To develop a postprocessing algorithm that enhances the visibility of intracranial venous vasculature and reduces the artifacts in the display of susceptibility-weighted images (SWI).

MATERIALS AND METHODS

Image-domain high-pass filters based on second-order phase difference were applied to the complex 3D SWI data to enhance the susceptibility phase shift of the veins and suppress background signal in SWI. A multivariant statistical parameter was used to suppress the noise in air.

RESULTS

Magnetic resonance (MR) venography with enhanced susceptibility phase shift and reduced off-resonance artifacts was obtained using the proposed filters. The background signal in the 3D MR venography data was well suppressed. Venous vasculature in the peripheral regions of the brain was well depicted and the adverse effect of noise in air in the maximum-intensity projection display of the 3D SWI data was well suppressed.

CONCLUSION

Image-domain high-pass filtering with second-order phase difference provides an alternative display of 3D SWI data with enhanced visibility of the venous vasculature and effective suppression of artifacts.

Segmentation of arteries in MPRAGE images of the ventral medial prefrontal cortex

A method for removing arteries that appear bright with intensities similar to white matter in Magnetized Prepared Rapid Gradient Echo images of the ventral medial prefrontal cortex is described. The Fast Marching method is used to generate a curve within the artery. Then, the largest connected component is selected to segment the artery which is used to mask the image. The surface reconstructed from the masked image yielded cortical thickness maps similar to those generated by manually pruning the arteries from surfaces reconstructed from the original image. The method may be useful in masking vasculature in other cortical regions.

Magnetic resonance angiography: From anatomical knowledge modeling to vessel segmentation

Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.

Region-growing segmentation of brain vessels: an atlas-based automatic approach

PURPOSE

To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA).

MATERIAL AND METHODS

An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process.

RESULTS

The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods.

CONCLUSION

The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.

Noise reduction in MR angiography with nonlinear anisotropic filtering

PURPOSE

To evaluate three-dimensional nonlinear anisotropic filtering in suppressing image noise in high spatial resolution magnetic resonance angiograms (MRA) acquired with hybrid undersampled projection reconstruction and phase contrast vastly undersampled isotropic projection reconstruction (PC-VIPR).

MATERIALS AND METHODS

Three-dimensional nonlinear anisotropic filtering was quantitatively analyzed and evaluated through the measurement of contrast to noise ratio (CNR) in PC-VIPR images and contrast enhanced peripheral MRA images. To filter MRA images with ultra-high spatial resolution and poor CNR, a spatial frequency dependent nonlinear anisotropic filtering algorithm was proposed that uses two-step processing to filter the whole spatial frequency data.

RESULTS

Three-dimensional nonlinear anisotropic filtering was shown to be effective in suppressing noise and improving CNR in MRA with isotropic spatial resolution. Higher CNR was achieved using spatial frequency dependent nonlinear anisotropic filtering. A typical CNR gain of between 50-100% was shown in our studies.

CONCLUSION

Three-dimensional nonlinear anisotropic filtering significantly improved CNR in MRA images with isotropic spatial resolution. Spatial frequency dependent nonlinear anisotropic filtering further improved CNR for MRA images with ultra-high spatial resolution and low CNR.

A review on MR vascular image processing algorithms: acquisition and prefiltering: part I

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.

Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images

The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. The luminal boundary detection was validated on contrast-enhanced (CE) MRA images of five stenotic phantoms. The results from the automated analysis correlated very well with the true diameters of the phantoms used in the in vitro study (r = 0.999, P < 0.001). MRA and x-ray angiography (XA) of the phantoms also correlated well (r = 0.895, P < 0.001). The average unsigned difference between the MRA and XA measurements was 0.08 +/- 0.05 mm. In conclusion, the automated approach allows the accurate assessment of vessel dimensions in MRA images.

Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: Results of an in-vitro evaluation

Three-dimensional rotational angiography (3DRA) is a new and promising technique for obtaining high-resolution isotropic 3D images of vascular structures. However, due to the relatively high noise level and the presence of other background structures in clinical 3DRA images, noise reduction is inevitable. In this paper, we evaluate a number of linear and nonlinear diffusion techniques for this purpose. Specifically, we analyze the effects of these techniques on the threshold-based visualization and quantification of vascular anomalies in 3DRA images. The results of in-vitro experiments indicate that edge-enhancing anisotropic diffusion filtering is most suitable: the increase in the user-dependency of visualizations and quantifications is considerably less with this technique compared to linear filtering techniques, and it is better at reducing noise near edges than isotropic nonlinear diffusion. However, in view of the memory and computation-time requirements of this technique, the latter scheme may be considered a useful alternative.

White and black blood volumetric angiographic filtering: ellipsoidal scale-space approach

Prefiltering is a critical step in three-dimensional (3-D) segmentation of the blood vessel and its display (see the recent book by Suri et al.). This paper presents a scale-space approach for filtering the white blood and black blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to isotropic volume followed by 3-D higher order separable Gaussian derivative convolution with known scales to generate edge volume. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3-D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the nonvasculature and background structures yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) images. The system is run over 20 patient studies from different areas of the body such as the brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 s of processing time per study for a data size of 512 x 512 x 194, which includes the complete performance evaluation. We also compare our strategy with the recently published MR filtering algorithms by Alexander et al. and Sun et al.

[An automated detection of lacunar infarct regions in brain MR images: preliminary study]

The purpose of this study is to develop a technique to detect lacunar infarct regions automatically in brain MR images. Our detection method is based on the definition of lacunar infarcts. After inputted images were binarized, we used feature values such as area, circularities and the center of gravity of candidate regions to extract isolated lacunar infarct regions. We also developed and used a new filter to enhance the signals of lacunar infarcts adjacent to some high intensity regions. 10 cases involving 81 sectional images were applied to our experiment. As a result, the sensitivity was 100% with approximately 1.77 false-positives per image. Our results are promising on the first stage, although it remains to improve on problems that to eliminate false-positives and automatically establish threshold value.

Hepatic MR angiography: a multiobserver comparison of visualization methods

OBJECTIVE

MR angiography (MRA) is an established diagnostic method; however, controversy remains over the best technique for display. In this study, we compared five methods of depicting hepatic MRA, including a novel skeletonization approach, using receiver operator characteristic (ROC) curves, interobserver variability (kappa values), and speed of interpretation.

SUBJECTS AND METHODS

Twenty-one patients scheduled for isolated liver perfusion therapy for metastatic disease underwent contrast-enhanced three-dimensional MRA to determine vascular anatomy. Vascular anatomy was validated at the time of surgery. We displayed the image data, using five techniques: maximum intensity projection, targeted maximum intensity projection, isointensity surface (isosurface), connected isointensity surface (connected isosurface), and ordered region growing skeleton (skeleton). Four observers, blinded to the surgical results, interpreted each technique in random order without patient identifiers. Areas under the ROC curves, kappa values of interobserver variability, and time to interpret each display were compared.

RESULTS

Skeletonized MRA had the highest area under the ROC curve (A(z), 0.90 +/- 0.04) compared with the other techniques (p < 0.013). Kappa scores of agreement were also highest for skeletonized MRA (0.75 +/- 0.04) and had no overlap at the 95% confidence level compared with other techniques. Compared with source images, all visualization methods were faster to interpret, but the skeleton technique was more quickly (p = 0.04) interpreted than the other techniques.

CONCLUSION

Skeletonized MRA with the skeleton connectivity algorithm is a semi-automated method of displaying complex arterial anatomy. Compared with other techniques, it is more accurate, more consistent among observers, and slightly faster to interpret. Skeletonization should be applicable to CT angiography and MRA.

A median filter for 3D FAST spin echo black blood images of cerebral vessels

High-resolution black-blood MRA images of intracranial vascular anatomy can be obtained using 3D fast spin-echo techniques. Although these images demonstrate excellent contrast between vessels and surrounding soft tissues, the dark signal from air and bone can obscure the desired vascular information when a minimum intensity projection image is created. In this paper, we describe an image processing technique based upon a median filter that is effective for detecting narrow vessel-like structures. Minimum intensity projection images of the filtered MRA volume can be obtained in any orientation without prior segmentation of the skull or surrounding air spaces. The filter is very effective for detecting and visualizing small vessels, but is much less effective for detecting vessels and vascular pathology larger than the filter detection width. The filtering technique is demonstrated on black-blood MRA images from a volunteer study.

Model-based quantitation of 3-D magnetic resonance angiographic images

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.

A post-processing technique for displaying vessels from routine fast-spin-echo images: MRI-derived angiography

Fast-spin-echo magnetic resonance (MR) images are routine components of a standard MR brain examination. On these images, blood vessels are visible as black flow void. We report that by applying an enhancement filter to a stack of routine fast-spin-echo MR images, projected angiographic images can be generated. The vascular detail in the projected image is similar to that observed in a phase-contrast image. In addition to its advantage in obtaining vessel information from routine images, the proposed post-processing technique is fast, easy to implement and completely automatic. These images provide additional vessel information that is useful when MR angiography is unavailable or as an aid in planning dedicated MR angiographic studies.