Quantitative Analysis for the Identification of Brain Tumor in CT and MRI Medical Images
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Abstract
The fusion in medical images is necessary, to derive the require information from multimodality medical images for diseases
diagnosis. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with
quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR
(pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion
techniques namely Redundancy Discrete Wavelet Transform (RDWT), Contourlet Transform and Multiple-Pulse Coupled Neural Network (MPCNN)
are applied. Further the fused image is analyzed with four types of quantitative metrics such as Standard Deviation (SD), Entropy (EN),
Overall Cross Entropy (OCE), and Power Signal to Noise Ratio (PSNR) for performance evaluation. From the experimental results we observed
that RDWT method provides better information (quality) using EN metric and the Contourlet Transform gives the difference in source to the
fused image using OCE metric and M-PCNN method offer the more contrast information using PSNR metric and also the fused image obtained
from the proposed fusion techniques has more information than the source images are proved through all metrics.
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Keywords: Medical image fusion; Multimodality images; Redundancy Discrete Wavelet Transform; Contourlet Transform; Multiple-Pluse
Coupled Neural Network; Quantitative Metrics
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