Fusion of medical images in wavelet domain: an algorithmic model

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Introduction: Image Fusion techniquesconsist of three stages: extraction of features, reduction of dimensions, and classification.
Problem: This paper presents a novel approach for Multiresolution analysis.
Objective: It is most widely used in image fusion science, which captures the features of an image not only at
different resolutions, but also at different orientations.
Methodology: This Wavelet based algorithm has additional advantages of fast implementation, versatility, auxiliary memory saving, complete reconstruction properties and simplicity as wavelet transformation was used.
Results: The simulation results of the MRI and CT images show perfectly acceptable image quality and cover
disease detection in the subsequent final image.
Conclusion: Principal Component Analysis (PCA) based on fusion algorithms will empower medical researchers or clinicians to properly apply image fusion and data transmission, which leads to better care practices to minimize redundancies and can also handle data loss.
Originality: Fusing images can decrease the image size, which can decrease the bandwidth when transmitting images. This also compresses the images; here an attempt is made to retain the same consistency.
Limitations: As this is still a relatively novel method, mistakes with regard to the handling of clinical data may prompt treatment deficiencies for the patient. Image quality must not be diminished with this usage
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