Digital breast tomosynthesis (DBT) has been investigated as a promising alternative to conventional X-ray
mammography for breast cancer screening. By reconstructing 3D volumetric images from multiple 2D projections
measured over a limited angular range, it can offer depth-directional information and improve both sensitivity
and specificity of cancer detection in dense breasts. The diagnostic performance of DBT can be affected by
a number of imaging parameters. The angular range of scan orbit is one of the most crucial factors, since
it determines the depth-directional resolution. Recently, we proposed the wide angle tomosynthesis based on
voltage modulations of X-ray source. By using X-rays with large penetration power on exterior positions, it
can acquire high-SNR projections over a wide angular range. In this paper, we present comparative studies on
exposure conditions in DBT, including narrow and wide angle scan using an invariant tube voltage of X-ray
source, and wide angle scan with the voltage modulation technique. In addition, we compared the conventional
reconstruction methods with recently proposed IDIR algorithms. In preliminary studies, the wide-angle scheme
with proposed IDIR algorithm showed superior performances in detecting abnormal lesions over conventional
approaches.
Recently, a multi-sensor image fusion system has been widely investigated due to its growing applications. In the
system, robust and accurate multi-modal image registration is essential and the fast registration is also important for
many applications. In this paper, we propose a fast algorithm for registering multi-modal images that are acquired from
two different sensors: electro-optic (EO) and infrared (IR). In the registration of multi-modal images, a normalized
mutual information (NMI) based registration algorithm is preferred due to its robust and accurate performance. And the
downhill simplex optimization scheme is popular in NMI-based registration because of its fast convergence rate.
However, since it still suffers from a high computational complexity, the complexity should be reduced further for (semi-
) real-time applications. In this paper, we attempt to reduce the computational complexity in the registration process. We
first modify the searching methodology for unconstrained function minimization in the ordinary downhill simplex
algorithm, by suggesting new vertex movements for fast vertex contraction. Thereby, we can reduce the number of
function evaluations. We also minimize the function evaluation time by linearizing the projective transformation in the
interpolation routine. Simulation results show that the proposed algorithm noticeably reduces the computational
complexity by 30% compared to the conventional NMI-based registration algorithm.
Motion estimation (ME) is one of the most time-consuming parts in video encoding system, and significantly affects the quality of the reconstructed sequences. In this paper, we propose a new fast algorithm, or the Feature Assisted Search Technique for Motion Estimation (FASTME), which is a gradient descent search combined with a new search strategy, namely, one-dimensional feature matching based on selective integral projections (1DFM). 1DFM assists a local search around an initial search center with a low computational complexity. After performing 1DFM, a small diamond search pattern and more compact horizontal and vertical search patterns, are adaptively used according to the result of 1DFM. The proposed algorithm outperforms existing fast algorithms in terms of speed up performance. In addition, its PSNR performance is better or similar to those of existing fast algorithms.
As many fast integer-pixel motion estimation algorithms have become available, an integer-pixel motion vector can be found by examining less than 10 search points. Meanwhile, 8 half-pixel positions around the integer-pixel motion vector are to be examined for half-pixel motion estimation. Hence, it becomes more meaningful to reduce the computational complexity of half-pixel motion estimation. In this paper, we propose a fast half-pixel motion estimation algorithm, by combining a directional search and linear modeling of SAD curve. The proposed algorithm reduces the number of search points to 2.21 in average, while the image quality of reconstructed sequences in terms of PSNR is similar to existing fast half-pixel motion estimation algorithms. In addition, by adjusting a user-defined parameter, the proposed algorithm can significantly reduce the number of search points to 0.34 on average with a slight PSNR degradation.
Existing fast block motion estimation algorithms, which reduce the computation by limiting the number of search points, utilize the motion vector (MV) characteristics of high spatial correlation as well as center-biased distribution in predicting an initial MV. Even though they provide good performance for slow motion sequences, they suffer from poor accuracy for fast or complex motion sequences. In this paper, a new fast and efficient block motion estimation algorithm is proposed. To find an initial search point, in addition to the predictors of zero MV and neighboring MVs, the algorithm utilizes another predictor obtained from one-dimensional feature matching using selective integral projections. This low complexity procedure enables the selection of a better initial search point so that a simple gradient descent search near this point may be enough to find the global minimum point. Compared to recent fast search algorithms, the proposed algorithm has lower computational complexity and provides better prediction performance, especially for fast or complex motion sequences.
The Optical Science Laboratory (OSL) has been involved in the development of large adaptive mirrors that takes a novel approach using metal mirror technology. This paper details the latest developments and preliminary test results of a prototype technology demonstrator.
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