54,773 research outputs found
CUDA based Level Set Method for 3D Reconstruction of Fishes from Large Acoustic Data
Acoustic images present views of underwater dynamics, even in high depths. With multi-beam echo sounders (SONARs), it
is possible to capture series of 2D high resolution acoustic images. 3D reconstruction of the water column and subsequent
estimation of fish abundance and fish species identification is highly desirable for planning sustainable fisheries. Main hurdles
in analysing acoustic images are the presence of speckle noise and the vast amount of acoustic data. This paper presents a level
set formulation for simultaneous fish reconstruction and noise suppression from raw acoustic images. Despite the presence of
speckle noise blobs, actual fish intensity values can be distinguished by extremely high values, varying exponentially from the
background. Edge detection generally gives excessive false edges that are not reliable. Our approach to reconstruction is based
on level set evolution using Mumford-Shah segmentation functional that does not depend on edges in an image. We use the
implicit function in conjunction with the image to robustly estimate a threshold for suppressing noise in the image by solving
a second differential equation. We provide details of our estimation of suppressing threshold and show its convergence as the
evolution proceeds. We also present a GPU based streaming computation of the method using NVIDIA’s CUDA framework to
handle large volume data-sets. Our implementation is optimised for memory usage to handle large volumes
2D antiscatter grid and scatter sampling based CBCT pipeline for image guided radiation therapy
Poor tissue visualization and quantitative accuracy in CBCT is a major
barrier in expanding the role of CBCT imaging from target localization to
quantitative treatment monitoring and plan adaptations in radiation therapy
sessions. To further improve image quality in CBCT, 2D antiscatter grid based
scatter rejection was combined with a raw data processing pipeline and
iterative image reconstruction. The culmination of these steps was referred as
quantitative CBCT, qCBCT. qCBCT data processing steps include 2D antiscatter
grid implementation, measurement based residual scatter, image lag, and beam
hardening correction for offset detector geometry CBCT with a bow tie filter.
Images were reconstructed with iterative image reconstruction to reduce image
noise. To evaluate image quality, qCBCT acquisitions were performed using a
variety of phantoms to investigate the effect of object size and its
composition on image quality. qCBCT image quality was benchmarked against
clinical CBCT and MDCT images. Addition of image lag and beam hardening
correction to scatter suppression reduced HU degradation in qCBCT by 10 HU and
40 HU, respectively. When compared to gold standard MDCT, mean HU errors in
qCBCT and clinical CBCT were 10 HU and 27 HU, respectively. HU inaccuracy due
to change in phantom size was 22 HU and 85 HU in qCBCT and clinical CBCT
images, respectively. With iterative reconstruction, contrast to noise ratio
improved by a factor of 1.25 when compared to clinical CBCT protocols. Robust
artifact and noise suppression in qCBCT images can reduce the image quality gap
between CBCT and MDCT, improving the promise of qCBCT in fulfilling the tasks
that demand high quantitative accuracy, such as CBCT based dose calculations
and treatment response assessment in image guided radiation therapy
Neural networks: Application to medical imaging
The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine
Blur resolved OCT: full-range interferometric synthetic aperture microscopy through dispersion encoding
We present a computational method for full-range interferometric synthetic
aperture microscopy (ISAM) under dispersion encoding. With this, one can
effectively double the depth range of optical coherence tomography (OCT),
whilst dramatically enhancing the spatial resolution away from the focal plane.
To this end, we propose a model-based iterative reconstruction (MBIR) method,
where ISAM is directly considered in an optimization approach, and we make the
discovery that sparsity promoting regularization effectively recovers the
full-range signal. Within this work, we adopt an optimal nonuniform discrete
fast Fourier transform (NUFFT) implementation of ISAM, which is both fast and
numerically stable throughout iterations. We validate our method with several
complex samples, scanned with a commercial SD-OCT system with no hardware
modification. With this, we both demonstrate full-range ISAM imaging, and
significantly outperform combinations of existing methods.Comment: 17 pages, 7 figures. The images have been compressed for arxiv -
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Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
Infrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions
Purpose: A time-efficient strategy to acquire high-quality multi-contrast
images is to reconstruct undersampled data with joint regularization terms that
leverage common information across contrasts. However, these terms can cause
leakage of uncommon features among contrasts, compromising diagnostic utility.
The goal of this study is to develop a compressive sensing method for
multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally
utilizes shared information while preventing feature leakage.
Theory: Joint regularization terms group sparsity and colour total variation
are used to exploit common features across images while individual sparsity and
total variation are also used to prevent leakage of distinct features across
contrasts. The multi-channel multi-contrast reconstruction problem is solved
via a fast algorithm based on Alternating Direction Method of Multipliers.
Methods: The proposed method is compared against using only individual and
only joint regularization terms in reconstruction. Comparisons were performed
on single-channel simulated and multi-channel in-vivo datasets in terms of
reconstruction quality and neuroradiologist reader scores.
Results: The proposed method demonstrates rapid convergence and improved
image quality for both simulated and in-vivo datasets. Furthermore, while
reconstructions that solely use joint regularization terms are prone to
leakage-of-features, the proposed method reliably avoids leakage via
simultaneous use of joint and individual terms.
Conclusion: The proposed compressive sensing method performs fast
reconstruction of multi-channel multi-contrast MRI data with improved image
quality. It offers reliability against feature leakage in joint
reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio
Surface-wave-enabled darkfield aperture for background suppression during weak signal detection
Sensitive optical signal detection can often be confounded by the presence of a significant background, and, as such, predetection background suppression is substantively important for weak signal detection. In this paper, we present a novel optical structure design, termed surface-wave-enabled darkfield aperture (SWEDA), which can be directly incorporated onto optical sensors to accomplish predetection background suppression. This SWEDA structure consists of a central hole and a set of groove pattern that channels incident light to the central hole via surface plasmon wave and surface-scattered wave coupling. We show that the surface wave component can mutually cancel the direct transmission component, resulting in near-zero net transmission under uniform normal incidence illumination. Here, we report the implementation of two SWEDA structures. The first structure, circular-groove-based SWEDA, is able to provide polarization-independent suppression of uniform illumination with a suppression factor of 1230. The second structure, linear-groove-based SWEDA, is able to provide a suppression factor of 5080 for transverse-magnetic wave and can serve as a highly compact (5.5 micrometer length) polarization sensor (the measured transmission ratio of two orthogonal polarizations is 6100). Because the exact destructive interference balance is highly delicate and can be easily disrupted by the nonuniformity of the localized light field or light field deviation from normal incidence, the SWEDA can therefore be used to suppress a bright background and allow for sensitive darkfield sensing and imaging (observed image contrast enhancement of 27 dB for the first SWEDA)
Design of an FPGA-based smart camera and its application towards object tracking : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
Smart cameras and hardware image processing are not new concepts, yet despite the fact both have existed several decades, not much literature has been presented on the design and development process of hardware based smart cameras. This thesis will examine and demonstrate the principles needed to develop a smart camera on hardware, based on the experiences from developing an FPGA-based smart camera. The smart camera is applied on a Terasic DE0 FPGA development board, using Terasic’s 5 megapixel GPIO camera. The algorithm operates at 120 frames per second at a resolution of 640x480 by utilising a modular streaming approach. Two case studies will be explored in order to demonstrate the development techniques established in this thesis.
The first case study will develop the global vision system for a robot soccer implementation. The algorithm will identify and calculate the positions and orientations of each robot and the ball. Like many robot soccer implementations each robot has colour patches on top to identify each robot and aid finding its orientation. The ball is comprised of a single solid colour that is completely distinct from the colour patches. Due to the presence of uneven light levels a YUV-like colour space labelled YC1C2 is used in order to make the colour values more light invariant. The colours are then classified using a connected components algorithm to segment the colour patches. The shapes of the classified patches are then used to identify the individual robots, and a CORDIC function is used to calculate the orientation.
The second case study will investigate an improved colour segmentation design. A new HSY colour space is developed by remapping the Cartesian coordinate system from the YC1C2 to a polar coordinate system. This provides improved colour segmentation results by allowing for variations in colour value caused by uneven light patterns and changing light levels
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