The extraction of objects from advanced geospatial intelligence (AGI) products based on synthetic aperture radar (SAR) imagery is complicated by a number of factors. For example, accurate detection of temporal changes represented in two-color multiview (2CMV) AGI products can be challenging because of speckle noise susceptibility and false positives that result from small orientation differences between objects imaged at different times. These cases of apparent motion can result in 2CMV detection, but they obviously differ greatly in terms of significance. In investigating the state-of-the-art in SAR image processing, we have found that differentiating between these two general cases is a problem that has not been well addressed. We propose a framework of methods to address these problems. For the detection of the temporal changes while reducing the number of false positives, we propose using adaptive object intensity and area thresholding in conjunction with relaxed brightness optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from a SAR AGI product. Results demonstrate the ability of our techniques to reduce false positives up to 60%.
Measuring the glomerular number in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. Commonly used approaches either require destruction of the entire kidney or perform extrapolation from measurements obtained from a few isolated sections. A recent magnetic resonance imaging (MRI) method, based on the injection of a contrast agent (cationic ferritin), has been used to effectively identify glomerular regions in the kidney. In this work, we propose a robust, accurate, and low-complexity method for estimating the number of glomeruli from such kidney MRI images. The proposed technique has a training phase and a low-complexity testing phase. In the training phase, organ segmentation is performed on a few expert-marked training images, and glomerular and non-glomerular image patches are extracted. Using non-local sparse coding to compute similarity and dissimilarity graphs between the patches, the subspace in which the glomerular regions can be discriminated from the rest are estimated. For novel test images, the image patches extracted after pre-processing are embedded using the discriminative subspace projections. The testing phase is of low computational complexity since it involves only matrix multiplications, clustering, and simple morphological operations. Preliminary results with MRI data obtained from five kidneys of rats show that the proposed non-invasive, low-complexity approach performs comparably to conventional approaches such as acid maceration and stereology.
KEYWORDS: Video, Visualization, Digital filtering, Control systems, Image filtering, Signal to noise ratio, Motion estimation, Video processing, Motion analysis, Eye
With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.
In surgical preparation, physicians will often utilize multimodal imaging scans to capture complementary information to improve diagnosis and to drive patient-specific treatment. These imaging scans may consist of data from magnetic resonance imaging (MR), computed tomography (CT), or other various sources. The challenge in using these different modalities is that the physician must mentally map the two modalities together during the diagnosis and planning phase. Furthermore, the different imaging modalities will be generated at various resolutions as well as slightly different orientations due to patient placement during scans. In this work, we present an interactive system for multimodal data fusion, analysis and visualization. Developed with partners from neurological clinics, this work discusses initial system requirements and physician feedback at the various stages of component development. Finally, we present a novel focus+context technique for the interactive exploration of coregistered multi-modal data.
Many important applications in clinical medicine can benefit from the fusion of spectroscopy data with anatomical
images. For example, the correlation of metabolite profiles with specific regions of interest in anatomical tumor images
can be useful in characterizing and treating heterogeneous tumors that appear structurally homogeneous. Such
applications can build on the correlation of data from in-vivo Proton Magnetic Resonance Spectroscopy Imaging (1HMRSI) with data from genetic and ex-vivo Nuclear Magnetic Resonance spectroscopy. To establish that correlation, tissue samples must be neurosurgically extracted from specifically identified locations with high accuracy. Toward that end, this paper presents new neuronavigation technology that enhances current clinical capabilities in the context of neurosurgical planning and execution. The proposed methods improve upon the current state-of-the-art in neuronavigation through the use of detailed three dimensional (3D) 1H-MRSI data. MRSI spectra are processed and analyzed, and specific voxels are selected based on their chemical contents. 3D neuronavigation overlays are then generated and applied to anatomical image data in the operating room. Without such technology, neurosurgeons must rely on memory and other qualitative resources alone for guidance in accessing specific MRSI-identified voxels. In contrast, MRSI-based overlays provide quantitative visual cues and location information during neurosurgery. The proposed methods enable a progressive new form of online MRSI-guided neuronavigation that we demonstrate in this study through phantom validation and clinical application.
Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited.
Artificial displacement (the apparent motion of stationary objects) is one important component of atmospheric
turbulence distortion, which has led many researchers to propose motion compensation as a solution. Defining a
sufficiently dense set of motion estimates for successful restoration is challenging, particularly for time sensitive
applications. We introduce a new, control grid implementation of optical
flow that allows for rapid and analytical
solutions to the motion estimation problem. Our results demonstrate the effectiveness of using the resulting
motion field for removing articial displacements in turbulence distorted videos.
KEYWORDS: Image resolution, Image filtering, Charge-coupled devices, Image interpolation, CCD image sensors, Lithium, RGB color model, Control systems, Digital signal processing, Detection and tracking algorithms
We recently reported good results with our image interpolation algorithm, One-Dimensional Control Grid Interpolation
(1DCGI), in the context of grayscale images. 1DCGI has high quantitative accuracy, flexibility with
respect to scaling factor, and low computational cost relative to similarly performing methods. Here we look to
extend our method to the demosaicing of Bayer-Patterned images. 1DCGI-based demosaicing performs quantitatively
better than the gradient-corrected linear interpolation method of Malvar. We also demonstrate effective
interpolation of full color images.
Goals for treating congenital heart defects are becoming increasingly focused on the long-term, targeting solutions that
last into adulthood. Although this shift has motivated the modification of many current surgical procedures, there
remains a great deal of room for improvement. We present a new methodological component for tetralogy of Fallot
(TOF) repair that aims to improve long-term outcomes. The current gold standard for TOF repair involves the use of
echocardiography (ECHO) for measuring the pulmonary valve (PV) diameter. This is then used, along with other
factors, to formulate a Z-score that drives surgical preparation. Unfortunately this process can be inaccurate and requires
a mid-operative confirmation that the pressure gradient across the PV is not excessive. Ideally, surgeons prefer not to
manipulate the PV as this can lead to valve insufficiency. However, an excessive pressure gradient across the valve
necessitates surgical action. We propose the use of computational fluid dynamics (CFD) to improve preparation for TOF
repair. In our study, pre-operative CT data were segmented and reconstructed, and a virtual surgical operation was then
performed to simulate post-operative conditions. The modified anatomy was used to drive CFD simulation. The
pressure gradient across the pulmonary valve was calculated to be 9.24mmHg, which is within the normal range. This
finding indicates that CFD may be a viable tool for predicting post-operative pressure gradients for TOF repair. Our
proposed methodology would remove the need for mid-operative measurements that can be both unreliable and
detrimental to the patient.
KEYWORDS: Magnetic resonance imaging, Digital filtering, Image segmentation, Signal to noise ratio, Fuzzy logic, Image filtering, Linear filtering, 3D image processing, Optical filters, Phase contrast
Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging
(PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality
more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that
target the early detection of problematic flow conditions. Segmentation is one component of this type of application that
can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem
that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and
can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present
a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour
modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise.
The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital
heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the
adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF
significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these
results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should
be used instead of scalar techniques.
The total cavopulmonary connection (TCPC) is a palliative surgical repair performed on children with a single ventricle (SV) physiology. Much of the power produced by the resultant single ventricle pump is consumed in the systemic circulation. Consequently the minimization of power loss in the TCPC is imperative for optimal surgical outcome. Toward this end we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation (ACGI) to function as a precursor to computational fluid dynamics (CFD) analysis aimed at quantifying power loss. Our technique combines positive aspects of optical flow-based and block-based motion estimation algorithms to accurately augment insufficiently dense Magnetic Resonance (MR) data sets with a minimal degree of computational complexity. The resulting enhanced data sets are used to reconstruct vascular geometries, and the subsequent reconstructions can then be used in conjunction with CFD simulations to offer th pressure and velocity information necessary to quantify power loss in the TCPC. Collectively these steps form a tool that transforms conventional MR data into more powerful information allowing surgical planning aimed at producing optimal TCPC configurations for successful surgical outcomes.
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