Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g.
lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally
provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An
alternative solution is to access the required computation power over the internet from a cloud computing service, which
is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation
resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this
paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We
parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the
standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a
very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce
implementation might become necessary in the future. All the experiments for this paper, including development and
testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.
Video surveillance has become one of the most important tools for public safety and security. In this paper, we present
our new work on developing efficient parallel computing algorithms and schemes for implementing H.264 video encoder
engine on Cell Broadband Engine (CBE) blade for high performance large scale video surveillance applications.
Extending our previous work on H.264 video encoding on CBE, we have developed new parallel computing schemes in
computational load partition, dynamic task scheduling, motion estimation, and mode selection. We partition the intensive
H.264 video encoding computation load into four major functional modules, namely, the pre-processing module, the
motion estimation module, the mode selection and transform/quantization module, and the Context Adaptive Binary
Arithmetic Coding (CABAC) module. The task scheduler dynamically assigns a waiting computing task to a SPE as
soon as it becomes available. Our new implementation has achieved more than 5X performance improvement to encode
32 standard-definition (SD 720x480 pixel resolution) H.264 video streams simultaneously at 30 frames per second with
one Cell Blade that consists of 16 Synergistic Processor Elements (SPEs) and two control Power Processor Elements
(PPEs) or 448 SD channels of H.264 video streams on a single chassis Cell Blade Center with 14 Cell Blades.
As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for
automated image processing and analysis technique. Most medical imaging methods require human visual inspection
and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an
automated image segmentation and classification method that identifies tumor cell nuclei in medical images and
classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and
labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei
counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell
carcinoma), and the automated results are compared with the hand-counted results by a pathologist.
Proc. SPIE. 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications
KEYWORDS: Image compression, Video, Diagnostics, Computer programming, Medical imaging, Telecommunications, Video compression, Computed tomography, Data communications, Picture Archiving and Communication System
Digital medical images are rapidly growing in size and volume. A typical study includes multiple image "slices." These
images have a special format and a communication protocol referred to as DICOM (Digital Imaging Communications in
Medicine). Storing, retrieving, and viewing these images are handled by DICOM-enabled systems. DICOM images are
stored in central repository servers called PACS (Picture Archival and Communication Systems). Remote viewing
stations are DICOM-enabled applications that can query the PACS servers and retrieve the DICOM images for viewing.
Modern medical images are quite large, reaching as much as 1 GB per file. When the viewing station is connected to the
PACS server via a high-bandwidth local LAN, downloading of the images is relatively efficient and does not cause
significant wasted time for physicians. Problems arise when the viewing station is located in a remote facility that has a
low-bandwidth link to the PACS server. If the link between the PACS and remote facility is in the range of 1 Mbit/sec,
downloading medical images is very slow. To overcome this problem, medical images are compressed to reduce the size
for transmission. This paper describes a method of compression that maintains diagnostic quality of images while
significantly reducing the volume to be transmitted, without any change to the existing PACS servers and viewer
software, and without requiring any change in the way doctors retrieve and view images today.
In this paper we consider Wyner-Ziv video compression using rateless LDPC codes. It is shown that the advantages of using rateless LDPC codes in Wyner-Ziv video compression, in comparison to using traditional fixed-rate LDPC codes, are at least threefold: 1) it significantly reduces the storage complexity; 2) it allows seamless integration with mode selection; and 3) it greatly improves the overall system's performance. Experimental results on the standard CIF-sized sequence <i>mobile_and_calendar</i> show that by combining rateless LDPC coding with simple skip mode selection, one can build a Wyner-Ziv video compression system that is, at rate 0.2 bits per pixel, about 2.25dB away from the standard JM software implementation of the H.264 main profile, more than 8.5dB better than H.264 Intra where all frames are H.264 coded intrapredicted frames, and about 2.3dB better than the same Wyner-Ziv system using fixed-rate LDPC coding. In terms of encoding complexity, the Wyner-Ziv video compression system is two orders of magnitude less complex than the JM implementation of the H.264 main profile.
We analyze challenges in the current approaches to digital video surveillance solutions, both technically and financially.
We propose a Cell Processor based digital video surveillance platform to overcome those challenges and address ever
growing needs in enterprise class surveillance solutions capable of addressing multiple thousands camera installations.
To improve the compression efficiency we have chosen H.264 video compression algorithm which outperforms all
standard video compression schemes as of today.
The full search block-matching algorithm is the simplest, but computationally very intensive approach. In recent years there was substantial progress in block motion estimation algorithms. Important milestones on this path were such algorithms as two-dimensional logarithmic search, three-step search, four step search, and diamond search. All these methods try to minimize the amount of search points applying sum of absolute differences (SAD) or equivalent metrics for each point. There were some works that studied partial SAD (PSAD) but mostly concentrated on a constant decimation factor. What we tried to study in this work is the performance of one of the best block matching search algorithms in combination with adaptive PSAD as a matching metric. The idea is that we use original motion estimation based on spatio-temporal correlation method, but instead of using SAD as a matching metric we use PSAD with adaptively chosen decimation factor.
Our simulation results show that for high motion sequences PSNR degradation between full search and the proposed method was around 0.1-0.7 dB. The computational complexity reduction of 650-1700 times (compared with the full search) and 9 times (compared to the original method) is pretty big and may be well worth this video quality decrease. In the case of more static sequences PSNR degradation between full search and the original motion estimation method was around 0 dB. When we compare the original method with the proposed one the degradation increases to 0.1 dB. The computational complexity reduction was around 1600-1700 times (compared with the full search) and 5-7 times (compared to the original method).
Low power dissipation and fast processing time are crucial requirements for embedded multimedia devices. This paper presents a technique in video coding to decrease the power consumption at a standard video decoder. Coupled with a small dedicated video internal memory cache on a decoder, the technique can substantially decrease the amount of data traffic to the external memory at the decoder. A decrease in data traffic to the external memory at decoder will result in multiple benefits: faster real-time processing and power savings. The encoder, given prior knowledge of the decoder’s dedicated video internal memory cache management scheme, regulates its choice of motion compensated predictors to reduce the decoder’s external memory accesses. This technique can be used in any standard or proprietary encoder scheme to generate a compliant output bit stream decodable by standard CPU-based and dedicated hardware-based decoders for power savings with the best quality-power cost trade off. Our simulation results show that with a relatively small amount of dedicated video internal memory cache, the technique may decrease the traffic between CPU and external memory over 50%.