DocumentCode :
229068
Title :
Visual analysis of large dental imaging data in caries research
Author :
Guangchen Ruan ; Hui Zhang
Author_Institution :
Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, USA
fYear :
2014
fDate :
9-10 Nov. 2014
Firstpage :
77
Lastpage :
84
Abstract :
With dental imaging data acquired at unprecedented speed and resolution, traditional serial image processing and single-node storage need to be re-examined in a “BigData” context. Furthermore, most previous dental computing has focused on the actual imaging acquisition and image analysis tools, while much less research has focused on enabling caries assessment via visual analysis of large dental imaging data. In this paper we present DENVIS, an end-to-end solution for cariologists to manage, mine, visualize, and analyze large dental imaging data for investigative carious lesion studies. DENVIS consists of two main parts: data driven image analysis modules triggered by imaging data acquisition that exploit parallel MapReduce tasks and ingest visualization archive into a distributed NoSQL store, and user driven modules that allow investigative analysis at run time. DENVIS has seen early use by our collaborators in oral health research, where our system has been used to pose and answer domain-specific questions for quantitative assessment of dynamic carious lesion activities.
Keywords :
Big Data; SQL; data acquisition; data analysis; data mining; data visualisation; dentistry; medical image processing; parallel processing; BigData context; DENVIS; caries research; cariologist; data driven image analysis modules; distributed NoSQL store; dynamic carious lesion activities quantitative assessment; image analysis tools; imaging data acquisition; large dental imaging data analysis; large dental imaging data management; large dental imaging data mining; large dental imaging data visualization; oral health research; parallel MapReduce tasks; serial image processing; single-node storage; user driven modules; visual analysis; Computed tomography; Dentistry; Image segmentation; Lesions; Teeth; Visualization; MapReduce; dental computing; visual knowledge discovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Large Data Analysis and Visualization (LDAV), 2014 IEEE 4th Symposium on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/LDAV.2014.7013207
Filename :
7013207
Link To Document :
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