Investigating the Role of Fractals in Enhancing MRI Image Compression Techniques and Signal Processing Efficiency

Abstract

Magnetic Resonance Imaging (MRI) provides significant medical benefits by aiding physicians in clinical staging and
predicting surgical extent. A substantial quantity of MRI images necessitates considerable storage capacity and
transmission rates within the system for offline preservation and diagnosis from afar. The enlargement of superiorquality
MRI pictures is very research-focused. Current MRI image compression algorithms that achieve substantial
compression ratios result in loss of data regarding tumors, which can lead to misinterpretation; conversely, systems
with low compression ratios fail to produce the desired outcomes. This research proposes a rapid fractal-based
reduction technique for MRI pictures. Initially, three-dimensional (3D) MRI pictures are transformed into a twodimensional
(2D) image series, enabling the sequence to utilize fractal compression. Range and area blocks are
categorized based on the intrinsic spatiotemporal resemblance of three-dimensional objects. Applying self-similarity
decreases the number of blocks in the comparing pool, enhancing the comparing speed of the suggested approach. A
residual correction approach is implemented to attain high-quality decompression of MRI images using compressing.
The experiments indicate that the reduction speed has increased by 2 to 3 times, and the Peak Signal Noise Ratio
(PSNR) has increased by about 10. The suggested approach resolves the conflict between significant compression
coefficients and the standard of MRI medical pictures.