Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom

PURPOSE: develop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles. METHOD AND MATERIALS: using the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest). RESULTS: an average of 8.87+/-5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.     

Motion mapping and mode decision for MPEG-2 to H.264/AVC transcoding

This paper describes novel transcoding techniques aimed for low-complexity MPEG-2 to H.264/AVC transcoding. An important application for this type of conversion is efficient storage of broadcast video in consumer devices. The architecture for such a system is presented, which includes novel motion mapping and mode decision algorithms. For the motion mapping, two algorithms are presented. Both efficiently map incoming MPEG-2 motion vectors to outgoing H.264/AVC motion vectors regardless of the block sizes that the motion vectors correspond to. In addition, the algorithm maps motion vectors to different reference pictures, which is useful for picture type conversion and prediction from multiple reference pictures. We also propose an efficient rate-distortion optimised macroblock coding mode decision algorithm, which first evaluates candidate modes based on a simple cost function so that a reduced set of candidate modes is formed, then based on this reduced set, we evaluate the more complex Lagrangian cost calculation to determine the coding mode. Extensive simulation results show that our proposed transcoder incorporating the proposed algorithms achieves very good rate-distortion performance with low complexity. Compared with the cascaded decoder-encoder solution, the coding efficiency is maintained while the complexity is significantly reduced.

Utilizing endoscopic technology to reveal real-time proteomic alterations in response to chemoprevention

Cancer chemoprevention approaches use either pharmacological or dietary agents to impede, arrest or reverse the carcinogenic process. Although several agents have shown effectiveness against colon cancer, present intervention strategies provide only partial reduction. In this study, we utilized high-resolution endoscopy to obtain colon tumor biopsy specimens from Apc mutant mice before and after 2-wk sulindac intervention. To acquire information beyond genomics, proteome analysis using the ProteomeLab PF2D platform was implemented to generate 2-D protein expression maps from biopsies. Chromatograms produced common signature profiles between sulindac and nonsulindac treated samples, and contrasting profiles termed "fingerprints". We selected a double peak that appeared in tumor biopsies from sulindac-treated mice. Further analyses using MS sequencing identified this protein as histone H2B. The location of H2B in the 1(st) dimension strongly suggested PTM, consistent with identification of two oxidized methionines. While further studies on sulindac proteomic fingerprints are underway, this study demonstrates the feasibility and advantages of "real-time" proteomic analysis for obtaining information on biomarker discovery and drug activity that would not be revealed by a genetic assay. This approach should be broadly applicable for assessing lesion responsiveness in a wide range of translational and human clinical studies.     

Performance of a modified polling strategy for broadband wireless lans in a harsh fading environment

In this paper, we consider a radio-based communication network in which a single, high speed radio channel is shared by some plurality of small portable notebooks. Such a system has the potential to provide LAN-like service within buildings, allowing the portable "notebooks" to access the CPU and data resources of a wired network. In this harsh indoor fading environment, a base-station approach is used. We propose a modified polling scheme for the indoor radio LAN channel. The base station regularly polls each remote, in response to which the remote either generates a "keep alive" packet or a request packet, allowing the base to fetch the requested information from the wired network and scheduling a reply to that remote. The efficiency of such a scheme is studied and the expected turn-around delay as a function of network loading is approximately obtained. A system stability under such a scheme is derived. For a polling cycle of 20 milliseconds and a network containing 10 or 50 users, for example, the maximum link efficiency is 97% and 87%, respectively, assuming that the link speed is 5 Mbps.The Center for Telecommunications Research is an NSF Engineering Research Center. This work was supported under NSF grant #ECD88-1 1111.     

Toxicity of waters from the St. Lawrence River at Massena Area-of-Concern to the plankton species Selenastrum capricornutum and Ceriodaphnia dubia

In 1972, the US and Canada committed to restore the chemical, physical, and biological integrity of the Great Lakes Ecosystem under the first Great Lakes Water Quality Agreement. During subsequent amendments, part of the St. Lawrence River at Massena NY, and segments of three tributaries, were designated as one Area of Concern (AOC) due to various beneficial use impairments (BUIs). Plankton beneficial use was designated impaired within this AOC because phytoplankton and zooplankton population data were unavailable or needed “further assessment”. Contaminated sediments from industrial waste disposal have been largely remediated, thus, the plankton BUI may currently be obsolete. The St. Lawrence River at Massena AOC remedial action plan established two criteria which may be used to assess the plankton BUI; the second states that, “in the absence of community structure data, plankton bioassays confirm no toxicity impact in ambient waters”. This study was implemented during 2011 to determine whether this criterion was achieved. Acute toxicity and chronic toxicity of local waters were quantified seasonally using standardized bioassays with green alga Selenastrum capricornutum and water flea Ceriodaphnia dubia to test the hypothesis that waters from sites within the AOC were no more toxic than were waters from adjacent reference sites. The results of univariate and multivariate analyses confirm that ambient waters from most AOC sites (and seasons) were not toxic to both species. Assuming both test species represent natural plankton assemblages, the quality of surface waters throughout most of this AOC should not seriously impair the health of resident plankton communities.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Development of a measure for the information technology infrastructure construct

The goal of this research was to operationalize the information technology infrastructure (ITI) concept by identifying its underlying dimensions and establishing a valid and reliable measure for the construct. In order to accomplish this task with the highest degree of certainty, two approaches were sequentially applied: exploratory factor analysis and confirmatory factor analysis. In evaluating the confirmatory factor model, a step-by-step process was employed, assessing unidimensionality, reliability, discriminant validity, and nomological validity. The dimensions of ITI were determined to be: Chief Information Officer, IT Planning, IT Security, Technology Integration, Advisory Committee, Enterprise Model, and Data Administration. Researchers may now make use of this validated ITI measure to conduct investigations of ITI and other organizational measures. Implications of the ITI measure with other organizational initiatives such as electronic commerce and knowledge management are explored.