NON-LINEAR SPECTRAL IMAGING MICROSCOPY STUDIES OF HUMAN HYPERTROPHIC SCAR

Skin scar is unique to humans,the major significant negative outcome sustained after thermal injuries,traumatic injuries,and surgical procedures.Hypertrophic scar in human skin is investigated using non-linear spectral imaging microscopy.The high contrast images and spectroscopic intensities of collagen and elastic fibers extracted from the spectral imaging of normal skin tissue,and the normal skin near and far away from the hypertrophic scar tissues in a 10-year-old patient case are obtained.The results show that there are apparent differences in the morphological structure and spectral characteristics of collagen and elastic fibers when comparing the normal skin with the hypertrophic scar tissue.These differences can be good indicators to differentiate the normal skin and hypertrophic scar tissue and demonstrate that non-linear spectral imaging microscopy has potential to noninvasively investigate the pathophysiology of human hypertrophic scar.

A unified framework for exploring time-varying volumetric data based on block correspondence

Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific visualization.Although great advances have been made over the years,existing solutions typically focus on only one or two aspects of data analysis and visualization.A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still missing.Towards this goal,we present a novel approach for time-varying data visualization that encompasses keyframe identification,feature extraction and tracking under a single,unified framework.At the heart of our approach lies in the GPU-accelerated BlockMatch method,a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D voxels.Based on the results of dense correspondence,we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity measure.Furthermore,in conjunction with the graph cut algorithm,this framework enables us to perform fine-grained feature extraction and tracking.We tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.

Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations.The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’rule.This effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational cost.Furthermore,this enables reconstruction of the original data more accurately than existing methods.We demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid points.The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.