Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Events on December 4, 2015

Sidharth Kumar

Sidharth Kumar Presents:

A Scalable and Tunable Adaptive Resolution Parallel I/O Framework

December 4, 2015 at 2:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Abstract:

The increase in computational power of supercomputers is enabling complex scientific phenomena to be simulated at ever-increasing resolution and fidelity. With these simulations routinely producing large volumes of data, performing efficient I/O at this scale has become a very difficult task. Large-scale parallel writes is challenging due to the complex interdependencies between I/O middleware and hardware. Analytic-appropriate reads are traditionally hindered by bottlenecks in I/O access. Moreover, the two components of I/O, data generation from simulations (writes) and data exploration for analysis and visualization (reads), have substantially different data access requirements. Parallel writes, performed on supercomputers, often deploy aggregation strategies to permit large-sized contiguous access. Analysis and visualization, usually performed on computationally modest resources, require fast access to localized subsets or multiresolution representations of the data. This dissertation tackles the problem of parallel I/O while bridging the gap between large-scale writes and analytics-appropriate reads.

The focus of this work is to develop an end-to-end adaptive-resolution data movement framework that provides efficient I/O, while supporting the full spectrum of modern HPC hardware. This is achieved by developing technology for highly scalable and tunable parallel I/O, applicable both to traditional parallel data formats and to multiresolution data formats, which are directly appropriate for analysis and visualization. To demonstrate the efficacy of the approach, a novel library (PIDX) is developed that is highly tunable and capable of adaptive-resolution parallel I/O to a multiresolution data format. Adaptive resolution storage and I/O, which allows subsets of a simulation to be accessed at varying spatial resolutions, can yield significant improvements to both the storage performance and I/O time. The library provides a set of parameters that controls the storage format and the nature of data aggregation across the network; further, a machine learning-based model is constructed that tunes these parameters for the maximum throughput. This work is empirically demonstrated by showing parallel I/O scaling up to 768K cores within a framework flexible enough to handle adaptive resolution I/O.

Posted by: Nathan Galli

Dr. Huda Al-Ghaib, Assistant Professor, College of Technology and Computing, Utah Valley University Presents:

Temporal Mammogram Segmentation for Improving Early Breast Cancer Detection

December 4, 2015 at 2:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Abstract:

Screening mammography often incorporates a computer aided diagnosis (CAD) scheme in its procedure to increase the accuracy of detecting gradual changes in breast tissues. One method for detecting gradual changes in temporal mammograms is through registration algorithms. Most registration algorithms require segmented mammograms as their inputs. The performance of registration algorithms and, hence, the performance of the CADs, are directly proportional to the quality of the segmented mammograms. Segmented mammograms include breast landmarks such as the nipple, the breast boundary, and the pectoral muscle. In this research, an algorithm for pectoral muscle detection is presented. The proposed algorithm consists of three phases that include thresholding, two straight lines fitting, and cliff detection and curve fitting. To measure the usefulness of the presented algorithm, it has to be compared to a well-known algorithm in the open literature. Dataset of 45 mammogram pairs is used for both algorithms. Evaluation is done by two experienced radiologists. The radiologists determined that the presented algorithm provided inaccurate results for only 17.95% and 19.23% of the cases, for radiologist 1 and 2, respectively. While the other algorithm provided inaccurate results for 30.77% of the cases. The detected pectoral muscle is removed from mammograms with mediolateral oblique (MLO) view and applied to a registration algorithm. An iterative registration algorithm that uses structural similarity (SSIM) index is developed to compute the optimal transformation that maps information in a temporal mammogram pair. The performance of the SSIM algorithm is compared with those of the correlation (CORR) coefficient and mutual information (MI) algorithms. A dataset of 70 mammogram pairs is used as an input for the registration algorithm. It is shown that the SSIM outperforms the CORR and MI in terms of error rate.

Bio:
Dr. Al-Ghaib received her undergraduate degree in computer engineering from the University of Technology in Baghdad-­‐Iraq in 2006. She worked in the Ministry of Higher Education and Scientific Research from 2007-­‐2009. She is a recipient of a Fulbright Scholar in 2009 for which she earned her Masters' degree in electrical engineering in 2011 from the University of Alabama in Huntsville (UAH) and the Ph.D. in electrical engineering from the same institute in 2015. During her graduate studies at UAH she was awarded an utstanding graduate student in engineering in 2014. Dr. Al-Ghaib's research interests are in the area of pattern recognition and data mining with applications in medical imaging. She is the author/co-­‐author of more than 10 journal and conference articles. She is a member of IEEE. Dr. Al-Ghaib serves as a reviewer for two international journals and she is a committee member in several research and grants reviewing committees at UVU.

Posted by: Nathan Galli