Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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In the field of medical imaging. Image fusion is an important application for extracting complementary information from different modality. In this work, we propose a fusion algorithm using CDF9/7 wavelet based on lifting scheme with specified fusion rules to combine pairs of multispectral Magnetic Resonance Imaging (MRI) such as T1, T2. The experimental results of brain tumor show that the proposed algorithm preserves both edge and component information and also increases the efficiency of tumor detection. The parameters like mutual information MI, entropy EN, and spatial frequency SF, standard deviation SD are calculated to evaluate performance of proposed algorithm. Finally the results are compared with existing methods.
Abdelfatih Bengana. 2016. \u201cMRI-T1 and T2 Image Fusion for Brain Image using CDF Wavelet based on Lifting Scheme\u201d. Global Journal of Medical Research - K: Interdisciplinary GJMR-K Volume 15 (GJMR Volume 15 Issue K6): .
Crossref Journal DOI 10.17406/gjmra
Print ISSN 0975-5888
e-ISSN 2249-4618
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: Algeria
Subject: Global Journal of Medical Research - K: Interdisciplinary
Authors: Abdelfatih Bengana, Ismail Boukli Hacene, Mohamed El Amine Chikh (PhD/Dr. count: 0)
View Count (all-time): 131
Total Views (Real + Logic): 3960
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Publish Date: 2016 01, Tue
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Neural Networks and Rules-based Systems used to Find Rational and
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In the field of medical imaging. Image fusion is an important application for extracting complementary information from different modality. In this work, we propose a fusion algorithm using CDF9/7 wavelet based on lifting scheme with specified fusion rules to combine pairs of multispectral Magnetic Resonance Imaging (MRI) such as T1, T2. The experimental results of brain tumor show that the proposed algorithm preserves both edge and component information and also increases the efficiency of tumor detection. The parameters like mutual information MI, entropy EN, and spatial frequency SF, standard deviation SD are calculated to evaluate performance of proposed algorithm. Finally the results are compared with existing methods.
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