227,224 research outputs found
Comparing Image Quality in Phase Contrast sub X-Ray Tomography -- A Round-Robin Study
How to evaluate and compare image quality from different sub-micrometer
(sub) CT scans? A simple test phantom made of polymer microbeads is used
for recording projection images as well as 13 CT scans in a number of
commercial and non-commercial scanners. From the resulting CT images, signal
and noise power spectra are modeled for estimating volume signal-to-noise
ratios (3D SNR spectra). Using the same CT images, a time- and
shape-independent transfer function (MTF) is computed for each scan, including
phase contrast effects and image blur (). The SNR spectra
and MTF of the CT scans are compared to 2D SNR spectra of the projection
images. In contrary to 2D SNR, volume SNR can be normalized with respect to the
object's power spectrum, yielding detection effectiveness (DE) a new measure
which reveals how technical differences as well as operator-choices strongly
influence scan quality for a given measurement time. Using DE, both
source-based and detector-based sub CT scanners can be studied and their
scan quality can be compared. Future application of this work requires a
particular scan acquisition scheme which will allow for measuring 3D
signal-to-noise ratios, making the model fit for 3D noise power spectra
obsolete
Color image quality measures and retrieval
The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure.
Three algorithms are designed for visual representation:
(1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio.
(2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI).
(3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding.
Three algorithms are designed for image quality measure (IQM):
(1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain.
(2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured.
(3) A no-reference color IQM based upon colorfulness, contrast and sharpness
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Reformulating computer vision problems over Riemannian manifolds has
demonstrated superior performance in various computer vision applications. This
is because visual data often forms a special structure lying on a lower
dimensional space embedded in a higher dimensional space. However, since these
manifolds belong to non-Euclidean topological spaces, exploiting their
structures is computationally expensive, especially when one considers the
clustering analysis of massive amounts of data. To this end, we propose an
efficient framework to address the clustering problem on Riemannian manifolds.
This framework implements random projections for manifold points via kernel
space, which can preserve the geometric structure of the original space, but is
computationally efficient. Here, we introduce three methods that follow our
framework. We then validate our framework on several computer vision
applications by comparing against popular clustering methods on Riemannian
manifolds. Experimental results demonstrate that our framework maintains the
performance of the clustering whilst massively reducing computational
complexity by over two orders of magnitude in some cases
Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT)
Purpose: The number of pencil beam scanned proton therapy (PBS-PT) facilities equipped with cone-beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS-PT treatments of thoracic tumors, we assess the performance of Motion-Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA-ROOSTER) four-dimensional CBCT (4DCBCT) reconstruction for sparse-view CBCT data and a realistic data set of patients treated with proton therapy. Methods: Daily CBCT projection data for nine non-small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four-dimensional CBCT images were reconstructed applying the MA-ROOSTER and the conventional phase-correlated Feldkamp-Davis-Kress (PC-FDK) method. Image quality was assessed by visual inspection, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated. Results: Image quality for the 4DCBCT reconstructions using MA-ROOSTER was superior to the PC-FDK reconstructions and close to FDK images (median CNR: 1.23 [PC-FDK], 1.98 [MA-ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC-FDK], 4.76 [MA-ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC-FDK vs FDK], 0.31 [MA-ROOSTER vs FDK]). The improved image quality of MA-ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions. Conclusion: MA-ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS-PT treatments of (N)SCLC patients
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
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