[WIP]: Add medical image denoising demo for anisotropic diffusion

[m-wisell]

This WIP pull request adds a medical image denoising demo implementing Perona-Malik anisotropic diffusion for edge-preserving noise removal in medical images.

Addresses: Issue #3973

Note: This is an initial WIP submission to gather early feedback on the core implementation. I plan to add unit tests and complete README documentation in the next commit (within 3-4 days).

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Current Status

Core Implementation:

• Anisotropic diffusion solver using backward Euler time-stepping
• Modular design with separate utils package
• P1 Lagrange finite elements for spatial discretization
• Conjugate Gradient solver with Jacobi preconditioner

Image Processing Utilities:

• Image I/O with automatic normalization
• Image-to-mesh conversion (interpolation-based)
• Mesh-to-image conversion using scipy griddata
• Quality metrics: PSNR, SSIM, Edge Preservation Index

Test Data Included:

• Real mammogram sample (sample_mammogram.jpg) for immediate testing
• Pre-generated synthetic test image (synthetic_test.png)
• No dataset download required to run demo

Validated Results (Real Mammogram 512x512):

• PSNR: 35.52 dB
• SSIM: 0.8739
• Edge Preservation: 0.8042