Skin Lesion Image Analysis: A Beginner's Practice in Python Medical Image Processing

Core Information

• Project Name: skin-lesion-image-analysis
• Original Author: hbaris23
• Source: GitHub (link)
• Release Time: June 14, 2026

Core View

This is a Python desktop application project for beginners in medical image processing. It uses tools like OpenCV and scikit-image to implement preprocessing, lesion segmentation, and feature extraction for skin lesion images, suitable for biomedical engineering students and Python developers to learn. The project states that it is only for educational and research purposes and does not replace professional medical diagnosis.

This project focuses on the preliminary analysis of skin lesion images in the field of biomedical engineering. It provides an intuitive GUI in the form of a desktop application to help developers master the application skills of Python in medical image analysis. The design goal is to provide beginners with a clear-structured and fully functional reference case through the complete process (image loading → processing → result output).

Tech Stack

• OpenCV: Core image processing (reading, format conversion, filtering, transformation)
• scikit-image: Advanced scientific image processing (morphological operations, edge detection, texture analysis)
• NumPy: Efficient array storage and computation
• Tkinter: Desktop GUI construction
• Matplotlib: Result visualization

Core Functions

1. Preprocessing: Resizing, RGB to grayscale conversion, noise removal, contrast enhancement
2. Lesion Segmentation: Thresholding, edge detection, morphological operations, region growing
3. Feature Extraction: Morphology (area, perimeter, circularity, symmetry), color (dominant color, uniformity), texture analysis
4. Batch Processing: Multi-image analysis, results exported as CSV

The project adopts a modular design:

• deri_app.py: Main entry, responsible for GUI rendering and user interaction
• image_processing.py: Core algorithm module (preprocessing, segmentation, feature extraction)
• model.py/model_strong.py: Model-related code (may involve machine learning inference)
• batch_process.py: Batch processing logic (folder traversal, result summary)

Modular design improves code maintainability and reusability, facilitating independent testing of image processing logic.

Educational Value

• Provides a complete practical case for biomedical engineering students
• Shows Python developers how to transform image processing algorithms into practical applications

Practical Scenarios

• Auxiliary tool for preliminary skin disease screening
• Health monitoring (recording lesion changes)
• Medical education and training

Note: The project clearly states it is not for medical diagnosis, only for reference.

1. Medical Use Restriction: Only for education and research, cannot replace professional diagnosis
2. Technical Limitations: Relies on traditional image processing algorithms, limited effect on complex lesions or low-quality images
3. Improvement Directions: Can introduce deep learning technology to enhance analysis capabilities

Entry Value

• Low-threshold entry point: Covers the complete process from input to output
• Master key links: Data preprocessing, algorithm application, result visualization, data export

Design Insights

• Modular design: Separates UI and processing logic to improve code quality
• Practice-oriented: Consolidates medical image processing skills through actual projects

Suitable for developers who want to enter the field of medical image processing as a learning reference.