Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.

A method for reducing streaking artifacts in 4D-CT reconstruction by generating additional projections is proposed. This method uses optical flow to track anatomy motion across the complete set of projections and then uses this information to compute interpolated projections while compensating for breathing motion. Original and interpolated projections, all belonging to one respiratory phase, are used to reconstruct a 4D-CT volume. Experimental results showed that the proposed method reduces artifacts and blurring in reconstructed 4D-CT volumes and improves the image quality.

The two-dimensional (2-D) discrete cosine transform (DCT) and the subsequent quantization of the transform coefficients are two computationally demanding steps of any DCT-based video encoder. In this paper, we propose an efficient joint implementation of these two steps, where the precision in computing the DCT can be exchanged for a reduction in the computational complexity. First, the quantization is embedded in the DCT, thus eliminating the need to explicitly quantize the transform coefficients. A multiplierless integer implementation of the quantized DCT (QDCT) is then proposed that performs shift and add operations instead of full multiplications. A sequence of multiplierless QDCT algorithms is obtained with increasing precision and number of computations. Finally, further savings in computations are obtained by terminating the DCT computations whenever intermediate results indicate that the transform and quantization steps will likely result in a block of zero values. The proposed algorithms are applied to, and results are presented for, high-quality MPEG-2 and low bit rate H.263 video encoding.

The rapidly expanding field of multimedia communications has fueled significant research and development work in the area of real-time video encoding. Dedicated hardware solutions have reached maturity and cost-efficient hardware encoders are being developed by several manufacturers. However, software solutions based on a general purpose processor or a programmable digital signal processor (DSP) have significant merits. Toward this objective, we have developed a flexible framework for video encoding that yields very good computation-performance tradeoffs. The proposed framework consists of a set of optimized core components: motion estimation (ME), the discrete cosine transform (DCT), quantization, and mode selection. Each of the components can be configured to achieve a desired computation-performance tradeoff. The components can be assembled to obtain encoders with varying degrees of computational complexity. Computation control has been implemented within the proposed framework to allow the resulting algorithms to adapt to the available computational resources. The proposed framework was applied to MPEG-2 and H.263 encoding using Intel's Pentium/MMX desktop processor. Excellent speed-performance tradeoffs were obtained.

In this paper, we propose a flexible framework for DCT-based video encoding that yields very good computation performance tradeoffs. Each of the encoding components features a set of parameters that can be used to control its computational complexity and performance. A sequence of optimum parameter sets have been designed to obtain encoders with varying degrees of computational complexity. A computation control mechanism is proposed within the encoding framework to allow the encoding algorithm to adapt to the available computational resources. This will allow the encoder to run in real time on machines with different computing power levels, while also achieving the best possible reproduction quality. The proposed framework was applied to MPEG-2 and H.263 encoding. Our experimental results show that excellent speed-performance tradeoffs as well as accurate computation control can be obtained using the proposed method.

Summary form only given. This paper presents a motion estimation technique for the coding of video sequences that is based on long-term temporal prediction. The motion vector of a moving object is tracked from one frame to another using a projection method. The traced motion vector is used as a starting point for the motion estimation search algorithm. The motion estimation algorithm used is based on an optimum fast block matching algorithm. Combinations of both spatial and temporal prediction are also used to obtain a more accurate estimate of the motion vector of the current macroblock. An inaccurately predicted motion vector can have a significant negative impact on the motion estimation algorithm. It can force the search algorithm to be trapped in a local minimum, or to spend unnecessary computations to find the optimum motion vector. The accuracy of the predicted motion vector is estimated using a reliability measure that allows the motion search algorithm to decide whether to use temporal motion vector predictor, spatial motion vector predictor, or both. As a reliability measure we used the mean squared error between the traced motion vectors in the current frame and in the previous frame. The reliability measure increases if the motion vector belongs to a moving object with constant speed. If the reliability measure is smaller than a certain threshold, then we abandon the temporal prediction and use spatial prediction. If both prediction methods fail, we abandon the fast motion search and perform full-search motion estimation in the low-resolution images. The experimental results show that long-term prediction reduces the number of computations performed by the motion search algorithm by up to 20%, while obtaining essentially the same quality.

A typical MPEG-2 video encoder requires that DCT and quantization be performed in most cases. In this paper we show how to combine these two steps, reducing substantially the number of computations. The new nonlinear transform is called the quantized discrete cosine transform, or QDCT. We also introduce a new method to trade-off the computational complexity and the precision of the QDCT. Although the QDCT is independent of the input data, better trade-offs can be obtained by making it data dependent, which is appropriate in multimedia applications such as MPEG-2 video coding. The results presented in this paper can also be extended to other linear transforms and/or other coding methods.

This paper presents a motion estimation technique for the coding of moving video sequences that is based on spatial and temporal prediction. The motion vector of a moving object is tracked using spatial and temporal prediction and used as a starting point for the motion estimation search algorithm. The predicted motion vector is selected from several candidate motion vectors according to the block matching criterion. Experimental results show that spatio-temporal prediction reduces the number of computations performed by the motion search algorithm by 30% for MPEG-2 encoding and by 40% for H.263 encoding.

This paper addresses the problem of transmission of real-time cardiology ultrasound video over telephone and ISDN lines. The high compression ratios needed for this application are achieved using a nonlinear model-based decomposition of the ultrasound sequence into structure and text are components. These components are encoded using a three-dimensional subband coder and an entropy-constrained residual vector quantizer, respectively. The new algorithm is implementable in real time on currently available hardware and achieves more than two-fold quality improvement over H.263.

This paper describes a very computationally efficient design algorithm for color image coding at low bit rates. The proposed algorithm is based on uniform tree-structured subband decomposition, multistage scalar quantization of the image subbands, and high order entropy coding. The main advantage of the algorithm is that no multiplications are required in both analysis/synthesis and encoding/decoding. This can lead to a simple hardware implementation of the subband coder, while maintaining a high level of performance.