Medical Image Fusion Based on Mamdani Type Min-sum Mean-of- max and Redundancy DWT Techniques with Quantitative Analysis
Volume: 10 - Issue: 02 - Date: 01-02-2021
Approved ISSN: 2278-1412
Published Id: IJAECESTU305 | Page No.: 140-145
Author: Deepali Pandey
Co- Author: Chhatrapani Gautam
Abstract:-Medical image fusion has revolutionized medical analysis by raising the
preciseness and performance of computer assisted diagnosing. This fused image is a lot
of productive as compared to its original input images. The fusion technique in medical
images is beneficial for resourceful disease diagnosing purpose. This paper illustrates
completely different multimodality medical picture combination method and their
consequences evaluate with various quantitative metrics. Firstly 2 registered pictures CT
(anatomical information) and MRI-T2 (functional information) are taken as input. Then
the fusion techniques are applied onto the input pictures such as Mamdani kind
minimum-sum-mean of maximum (MIN-SUM-MOM) and Redundancy discrete wave
transform (RDWT) and so the resulting fused image is analyzed with quantitative metrics
namely Over all irritated Entropy, Peak Signal –to- Noise ratio (PSNR), Signal to Noise
ratio (SNR), Structural Similarity Index(SSIM), Mutual Information(MI). From the
derived results it's inferred that Mamdani type MIN-SUM-MOM is more productive than
RDWT and also the projected fusion techniques provide additional info compared to the
input images as justified by all the metrics.
Key Words:-Medical image analysis, RDWT, Image fusion, Deep Learning
Area:-Engineering
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