Image Preparation for Machine Learning and Visualization GUI using PyQt

This GUI contains the commonly used image preprocessing techniques in machine learning and deep learning in medical image taking into consideration different ground truth types. The input can be 2D and 3D images.

Intnsity matching:

• Histogram matching
• Histogram qualization and
• Intensity normalization

Patch extraction:

• 2D
• 2.5D and
• 3D

Data augmentation:

• Flipping
• Rotation

Libraries Used

The code has been tested with the following configuration

• PyQt5
• OpenCV-Python == 3.4.0
• Pydicom == 0.9.9
• nibabel == 2.1.0
• nipype == 0.12.1
• python == 3.6
• scipy == 0.19.0
• sckit-image == 0.13.0
• sckit-learn == 0.18.1
• Qimage2ndarray == 1.6

How to run

Once you have installed the above softwares run main.py file

Results

More detail about the project can be found in Project_report.pdf file.