DocumentCode :
2958051
Title :
Using affinity set on mining the necessity of computed tomography scanning
Author :
Chen, Yuh-Wen ; Larbani, Moussa ; Li, Tzung-Hung ; Chen, Chao-Wen
Author_Institution :
Da-Yeh Univ., Changhua, Taiwan
fYear :
2009
fDate :
22-24 July 2009
Firstpage :
219
Lastpage :
223
Abstract :
Computed tomography (CT) is a medical imaging method of tomography. Digital geometry processing is used to generate a three-dimensional image of the inside of a patient from a large series of two-dimensional X-ray images taken around a single axis of rotation. The scanning of CT has become an important tool in medical imaging to supplement X-rays and medical ultrasonography. Although it is expensive, it is the best tool to diagnose a large number of different disease entities; especially, for the trauma patients in emergency room. In this study, the trauma patients, who were treated by the CT scanning are collected in order to discover the critical knowledge; that is, what characteristics of trauma patients would lead to the necessity of CT scanning? The data mining model of affinity set and neural network (NN) are both used for resolution and comparison. Finally, studying results show that he affinity model performs better than the NN model, but the collected data lacks the explanatory power in practices. Thus, a further research is necessary.
Keywords :
X-ray imaging; biomedical ultrasonics; computerised tomography; data mining; medical image processing; neural nets; set theory; affinity set; computed tomography scanning; digital geometry processing; medical imaging method; medical ultrasonography; neural network; two-dimensional X-ray images; Biomedical imaging; Computed tomography; Diseases; Geometry; Image generation; Medical diagnostic imaging; Medical treatment; Neural networks; Ultrasonography; X-ray imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Service Operations, Logistics and Informatics, 2009. SOLI '09. IEEE/INFORMS International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-3540-1
Electronic_ISBN :
978-1-4244-3541-8
Type :
conf
DOI :
10.1109/SOLI.2009.5203933
Filename :
5203933
Link To Document :
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