DocumentCode :
2202568
Title :
Neural network based edge detection for automated medical diagnosis
Author :
Lu, Dingran ; Yu, Xiao-Hua ; Jin, Xiaomin ; Li, Bin ; Chen, Quan ; Zhu, Jianhua
Author_Institution :
Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
fYear :
2011
fDate :
6-8 June 2011
Firstpage :
343
Lastpage :
348
Abstract :
Edge detection is an important but rather difficult task in image processing and analysis. In this research, artificial neural networks are employed for edge detection based on its adaptive learning and nonlinear mapping properties. Fuzzy sets are introduced during the training phase to improve the generalization ability of neural networks. The application of the proposed neural network approach to the edge detection of medical images for automated bladder cancer diagnosis is also investigated. Successful computer simulation results are obtained.
Keywords :
edge detection; fuzzy set theory; learning (artificial intelligence); medical image processing; neural nets; adaptive learning; artificial neural networks; automated bladder cancer diagnosis; automated medical diagnosis; edge detection; fuzzy sets; image analysis; image processing; medical images; nonlinear mapping properties; Artificial neural networks; Cancer; Detectors; Gray-scale; Image edge detection; Laplace equations; Training; Artificial neural networks; Automatic medical diagnosis; Edge detection; Image processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2011 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4577-0268-6
Electronic_ISBN :
978-1-4577-0269-3
Type :
conf
DOI :
10.1109/ICINFA.2011.5949014
Filename :
5949014
Link To Document :
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