%0 Journal Article
	%A B. Al-Naami and  N. Gharaibeh and  A. AlRazzaq Kheshman
	%D 2013
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 77, 2013
	%T Automated Detection of Alzheimer Disease Using Region Growing technique and Artificial Neural Network
	%U https://publications.waset.org/pdf/11271
	%V 77
	%X Alzheimer is known as the loss of mental functions
such as thinking, memory, and reasoning that is severe enough to
interfere with a person's daily functioning. The appearance of
Alzheimer Disease symptoms (AD) are resulted based on which part
of the brain has a variety of infection or damage. In this case, the
MRI is the best biomedical instrumentation can be ever used to
discover the AD existence. Therefore, this paper proposed a fusion
method to distinguish between the normal and (AD) MRIs. In this
combined method around 27 MRIs collected from Jordanian
Hospitals are analyzed based on the use of Low pass -morphological
filters to get the extracted statistical outputs through intensity
histogram to be employed by the descriptive box plot. Also, the
artificial neural network (ANN) is applied to test the performance of
this approach. Finally, the obtained result of t-test with confidence
accuracy (95%) has compared with classification accuracy of ANN
(100 %). The robust of the developed method can be considered
effectively to diagnose and determine the type of AD image.
	%P 204 - 208