Cheuk L. Chan

Image sequence filtering in quantum-limited noise with applications to low-dose fluoroscopy

Clinical angiography requires hundreds of X-ray images, putting the patients and particularly the medical staff at risk. Dosage reduction involves an inevitable sacrifice in image quality. In this work, the latter problem is addressed by first modeling the signal-dependent, Poisson-distributed noise that arises as a result of this dosage reduction. The commonly utilized noise model for single images is shown to be obtainable from the new model. Stochastic temporal filtering techniques are proposed to enhance clinical fluoroscopy sequences corrupted by quantum mottle. The temporal versions of these filters as developed here are more suitable for filtering image sequences, as correlations along the time axis can be utilized. For these dynamic sequences, the problem of displacement field estimation is treated in conjunction with the filtering stage to ensure that the temporal correlations are taken along the direction of motion to prevent object blur.

Low bit rate video coding using robust motion vector regeneration in the decoder

In this paper, we present a novel coding technique that makes use of the nonstationary characteristics of an image sequence displacement field to estimate and encode motion information. We utilize an MPEG style codec in which the anchor frames in a sequence are encoded with a hybrid approach using quadtree, DCT, and wavelet-based coding techniques. A quadtree structured approach is also utilized for the interframe information. The main objective of the overall design is to demonstrate the coding potential of a newly developed motion estimator called the coupled linearized MAP (CLMAP) estimator. This estimator can be used as a means for producing motion vectors that may be regenerated at the decoder with a coarsely quantized error term created in the encoder. The motion estimator generates highly accurate motion estimates from this coarsely quantized data. This permits the elimination of a separately coded displaced frame difference (DFD) and coded motion vectors. For low bit rate applications, this is especially important because the overhead associated with the transmission of motion vectors may become prohibitive. We exploit both the advantages of the nonstationary motion estimator and the effective compression of the anchor frame coder to improve the visual quality of reconstructed QCIF format color image sequences at low bit rates. Comparisons are made with other video coding methods, including the H.261 and MPEG standards and a pel-recursive-based codec.

Spatiotemporal filtering of digital angiographic image sequences corrupted by quantum mottle

A method is described for the spatio-temporal filtering of digital angiographic image sequences corrupted by simulated quantum mottle. An x-ray dosage reduction in coronary imaging studies inevitably leads to the introduction of quantum mottle --a Poisson distributed, signal dependent noise that occurs as a result of statistical fluctuations in the arrival of photons at the image intensifier tube. Although spatial filtering of individual frames in the sequence is often performed to improve image quality, this technique does not utilize valuable information from temporal correlations between images. The spatio-temporal filter here estimates motion trajectories for individual pixels and then filters along the direction of motion. This method is different from temporal filtering techniques that do not use motion compensation as the latter always blur the edges of the coronary arteries. Although the method is derived for the estimation of a single frame from two degraded frames of a sequence, it is easily generalized to multi-frame estimates. The performance of the above filter is examined using real image sequences corrupted by quantum mottle.

Iterative maximum likelihood displacement field estimation in quantum-limited image sequences

We develop an algorithm for obtaining the maximum likelihood (ML) estimate of the displacement vector field (DVP) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, stereo matching, and frame registration in low-light level image sequences as well as low-dose clinical X-ray image sequences. In the latter case, a controlled X-ray dosage reduction may be utilized to lower the radiation exposure to the patient and the medical staff. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. A Fisher-Bayesian formulation is used to estimate the DVF and a block component search algorithm is employed in obtaining the solution. Several experiments involving a phantom sequence and a teleconferencing image sequence with realistic motion demonstrate the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions (20-25 events/pixel).

Recursive Locally Linear MMSE Motion-Compensated Image Sequence Filtering under Quantum-Limited Conditions

Abstract Quantum noise in image sequences arises in a wide array of applications including medical and astronomical images and remote sensing. It is an undesirable artifact caused by the unavailability or intentional depletion of X-ray and/or light photons necessary for imaging. In this paper, we develop a recursive sliding window, locally linear minimum mean squared error motion-compensated temporal filter for the enhancement of image sequences corrupted by this type of noise. The filter is derived from the formulation of a noise model which describes the underlying physical processes of quantum noise. The recursive implementation of this filter will provide an intuitive manner for addressing subsequent image frames as new observations when acquired under quantum-limited conditions. Experimental results are provided which show the effectiveness of the proposed estimator on both sequences simulated with quantum noise and on real clinical sequences containing natural quantum mottle.

Enhancement of low-dosage cine-angiographic image sequences using a modified expectation maximization algorithm

Clinical x-ray image sequences acquired through fluoroscopy systems may be corrupted by quantum mottle--a Poisson-distributed, signal-dependent noise that arises with a controlled x- ray dosage reduction in an attempt to lower the exposure to the patient and the medical staff. In this paper, an approach to temporally filter this sequence is presented. It relies on a joint estimation of the signal and the displacement field through a maximum likelihood approach. Implementation is done via a modified EM algorithm to facilitate a more tractable solution.

Nonlinear model-based spatio-temporal filtering of image sequences

A Volterra model-based spatio-temporal filter for the enhancement of noise-corrupted image sequences is considered. This model uses estimates of higher-order statistics (HOS) to filter non-wide-sense stationary (WSS) image sequences that cannot be correctly modeled by second-order statistics alone. Some results are shown for this filter when it is applied along the direction of motion in image sequences with simulated noise.<<ETX>>

Maximum a posteriori displacement field estimation in quantum-limited image sequences

In this paper, we develop an algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, and frame registration in low-light level image sequences as well as low-dose clinical x-ray image sequences. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. The specification of priors for the DVF allows a smoothness constraint for the vector field. In addition, discontinuities of the field are taken into account through the introduction of a line process for neighboring vectors. A Bayesian formulation is used in this paper to estimate the DVF and a block component algorithm is employed in obtaining a solution. Several experiments involving a phantom sequence show the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions.

Occlusion and nonstationary displacement field estimation in quantum-limited image sequences

In this paper, we develop an algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking and frame registration in low- light level image sequences as well as low-dose clinical x-ray image sequences. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. The specification of priors for the DVF allows a smoothness constraint for the vector field. In addition, discontinuities and areas corresponding to occlusions which are present in the field are taken into account through the introduction of both a line process and an occlusion process for neighboring vectors. A Bayesian formulation is used in this paper to estimate the DVF and a block component algorithm is employed in obtaining a solution. Several experiments involving a phantom sequence show the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions.