A Python-based GUI application for medical image visualization, processing, and analysis. This tool is designed for working with medical images in formats like DICOM and standard image formats.
- Image Loading: Load and display DICOM and other image formats (e.g., PNG, JPEG).
- Noise Addition: Add Gaussian, Salt & Pepper, or Poisson noise to the images.
- Denoising: Apply noise reduction methods such as Median, Bilateral, and Non-local Means filters.
- Contrast Enhancement: Improve image quality using:
- Histogram Equalization
- CLAHE (Contrast Limited Adaptive Histogram Equalization)
- Adaptive Gamma Correction
- ROI Selection: Select Regions of Interest (ROIs) for:
- Signal-to-Noise Ratio (SNR) calculation
- Contrast-to-Noise Ratio (CNR) calculation
- Histograms: Display pixel intensity histograms with image statistics.
- Zoom and Field of View: Dynamically adjust zoom and FOV using sliders and spinboxes. Select the interpolation method to be applied during zoom, such as:
- Nearest Neighbor
- Bilinear
- Cubic
- Python 3.x
- Libraries:
- PyQt5
- NumPy
- Matplotlib
- OpenCV
- Pillow
- pydicom
- scikit-image
- SciPy
You can install all dependencies using the requirements.txt file.
- Clone the repository:
git clone https://github.com/AbdelMoety/Medical-Image-Viewer.git
- Navigate to the project directory: cd MedicalImageApp
- Create a virtual environment and activate it: python -m venv venv source venv\Scripts\activate
- Install dependencies: pip install -r requirements.txt
- Run the application
- Use the GUI to: Load a medical image. Add noise or apply filters. Select ROIs for SNR and CNR calculations. Enhance contrast or zoom in on specific areas. View histograms and image statistics.
This project is licensed under the MIT License.
Built using PyQt5 and other powerful Python libraries. Inspired by the need for easy-to-use medical image analysis tools.