DocumentCode :
2248761
Title :
The rapid elicitation of knowledge about images using fuzzy information granules
Author :
Rossiter, Jonathan M.
Author_Institution :
RIKEN, Inst. of Phys. & Chem. Res., Nagoya, Japan
Volume :
2
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
1159
Abstract :
We present a new method for tagging image regions using uncertain information granules. This tagging forms an efficient route for the elicitation of knowledge from domain experts with respect to images. We then use this uncertain granular information to train a fuzzy machine learner and then to classify unseen images. This method is particularly suited to applications where an expert input into the classification process is essential but where the expert´s time is in extremely short supply. Results are presented within the example domain of detecting the lung disease from computed tomography scans.
Keywords :
computerised tomography; diseases; fuzzy set theory; image classification; knowledge acquisition; learning (artificial intelligence); lung; medical image processing; computed tomography scans; fuzzy information granules; fuzzy machine learner; image classification; image knowledge elicitation; image region tagging; lung disease detection; uncertain granular information; Computed tomography; Fuzzy control; Image databases; Image segmentation; Labeling; Machine learning; Predictive models; Shape; Tagging; X-ray imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
ISSN :
1098-7584
Print_ISBN :
0-7803-8353-2
Type :
conf
DOI :
10.1109/FUZZY.2004.1375575
Filename :
1375575
Link To Document :
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