Phosphor-converted Light Emitting Diodes (pc-LEDs) to generate white light from blue or UV emitting diodes can be made using Eu2+ doped nitridosilicates - M2Si5N8- and/or oxo nitridosilicates - MSi2O2N2 with M=alkaline earth. Luminescence properties of some out of this new class of color converters have been investigated. As expected from strong absorption the decay times of the internally excited Eu2+ is short (around 1 microsec) and depends on the cation sharing the unit cell. Time resolved spectroscopy illustrates the behavior even more clearly. Laboratory pcLEDs using 2 of the nitride phosphors show excellent drive and temperature stability of all color properties as expected.
Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need
to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.
KEYWORDS: Brain, Image segmentation, Neuroimaging, RGB color model, Digital filtering, Visualization, Magnetic resonance imaging, 3D visualizations, Image processing, Data modeling
We present a semi-automatic technique for segmenting a large cryo-sliced human brain data set that contains 753 high resolution RGB color images. This human brain data set presents a number of unique challenges to segmentation and visualization due to its size (over 7 GB) as well as the fact that each image not only shows the current slice of the brain but also unsliced deeper layers of the brain. These challenges are not present in traditional MRI and CT data sets. We have found that segmenting this data set can be made easier by using the YIQ color model and morphology. We have used a hardware-assisted interactive volume renderer to evaluate our segmentation results.
Advanced medical imaging technologies have enabled biologists and other researchers in biomedicine, biochemistry and bio-informatics to gain better insight in complex, large-scale data sets. These datasets, which occupy large amounts of space, can no longer be stored on local hard drives. San Diego Supercomputer Center (SDSC) maintains a large data repository, called High Performance Storage System (HPSS), where large-scale biomedical data sets can be stored. These data sets must be transmitted over an open or closed network (Internet or Intranet) within a reasonable amount of time to make them accessible in an interactive fashion to the researchers all over the world. Our approach deals with extracting, compressing and transmitting these data sets using the Haar wavelets, over a low- to medium-bandwidth network. These compressed data sets are then transformed and reconstructed into a 3-D volume on the client side using texture mapping in Java3D. These data sets are handled using the Scalable Visualization Toolkits provided by the NPACI (National Partnership for Advanced Computational Infrastructure). Sub-volumes of the data sets are extracted to provide a detailed view of a particular region of interest (ROI). This application is being ported to C++ platform to obtain higher rendering speed and better performance but lacks platform independency.
Conference Committee Involvement (4)
Visualization and Data Analysis 2015
9 February 2015 | San Francisco, California, United States
Visualization and Data Analysis 2013
4 February 2013 | Burlingame, California, United States
Visualization and Data Analysis 2010
18 January 2010 | San Jose, California, United States
Visualization and Data Analysis 2009
19 January 2009 | San Jose, California, United States
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