Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Measuring Lesion Growth from 3D Medical Images
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
AsiaSim'04 Proceedings of the Third Asian simulation conference on Systems Modeling and Simulation: theory and applications
Exploiting HPC resources for the 3D-time series analysis of caries lesion activity
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
| Hi-index | 0.00 |
Caries lesion activity assessment has been a routine diagnostic procedure in dental caries management, traditionally employing subjective measurements incorporating visual and tactile inspections. Recently, advances in 2D/3D image processing and analysis methods and microfocus x-ray computerized tomography (μ-CT) hardware, together with increased power of high performance computing, have created a synergic effect that is revolutionizing many fields in dental computing. In this paper, we report such an XSEDE-enabled high-throughput lesion activity assessment workflow that exploits 2D/3D image processing, visual analytics, and high performance computing technologies. Our paper starts with a brief introduction of the image dataset in our dental studies. We then proceed to a family of 2D image analysis, ROI segmentation, and 3D geometric construction methods. By combining dental imaging technology and 2D/3D image processing algorithms, we transform the task of lesion activity assessment into a 3D-time series analysis of computer generated lesion models. Building on the computational algorithms and implementation models, we develop a high-throughput dental computing workflow exploiting MapReduce tasks to parallelize the image analysis of dental CT scans, the segmentation of region-of-interest (ROI), and the 3D construction of lesion volumes. We showcase the employment of 3D-time series analysis and several other information representations that are applied to our lesion activity assessment scenario focusing on large scale dental image data.