%0 Journal Article
	%A Sri Hartati and 1Agus Harjoko and  Brad G. Nickerson
	%D 2009
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 32, 2009
	%T Fuzzy Hyperbolization Image Enhancement and Artificial Neural Network for Anomaly Detection
	%U https://publications.waset.org/pdf/10633
	%V 32
	%X A prototype of an anomaly detection system was
developed to automate process of recognizing an anomaly of
roentgen image by utilizing fuzzy histogram hyperbolization image
enhancement and back propagation artificial neural network.
The system consists of image acquisition, pre-processor, feature
extractor, response selector and output. Fuzzy Histogram
Hyperbolization is chosen to improve the quality of the roentgen
image. The fuzzy histogram hyperbolization steps consist of
fuzzyfication, modification of values of membership functions and
defuzzyfication. Image features are extracted after the the quality of
the image is improved. The extracted image features are input to the
artificial neural network for detecting anomaly. The number of nodes
in the proposed ANN layers was made small.
Experimental results indicate that the fuzzy histogram
hyperbolization method can be used to improve the quality of the
image. The system is capable to detect the anomaly in the roentgen
image.
	%P 2089 - 2092