Medical-Digital-Image-Preprocessing-and-Segmentation

Exploring fundamental digital image preprocessing and segmentation techniques in the context of medical imaging.

🔎 Overview

This project demonstrates key preprocessing and segmentation techniques applied to medical images, with a focus on brain tumor detection. Preprocessing improves image clarity while segmentation isolates critical regions of interest, providing a foundation for AI-based diagnostic models.

⚙️ Methods

• Grayscale conversion – removes unnecessary color, simplifying image analysis.
• Min–max normalization – enhances contrast by scaling pixel intensities.
• Thresholding segmentation – separates regions of differing intensities, useful for tumor isolation.

📊 Results

• Enhanced contrast and visibility of brain structures.
• Segmentation successfully highlighted abnormal regions.
• Images show strong potential for AI model training.

🌍 Real-World Applications

• AI training data for tumor detection models
• Computer-aided diagnosis in radiology
• Clinical decision support for faster, more accurate assessments

📚 Requirements

numpy
matplotlib
opencv-python
scikit-image