README.md

📷 Image Preprocessing Repository

Welcome to the Image Preprocessing Repository! This collection of image processing code snippets and notebooks aims to help you enhance and manipulate your images effectively. Explore the various techniques and tools available to preprocess your images for different applications.

📂 Repository Contents

• CLAHE.ipynb: Contrast Limited Adaptive Histogram Equalization (CLAHE) notebook.
• Circle Binary Mask.ipynb: Create binary masks in circular shapes.
• Data Augmentation: Folder with initial data augmentation scripts.
• Extracting all slices of a 3D image: Extract all slices from a 3D image.
• Extracting single slice of a 3D image: Extract a single slice from a 3D image.
• Gabor Filter.ipynb: Gabor filter implementation for image processing.
• Gamma Correction.ipynb: Adjust image gamma for enhanced visibility.
• Histogram Equalization.ipynb: Notebook on histogram equalization.
• Homomorphic Noise Removal.ipynb: Remove noise using the homomorphic filter.
• Hough Line Transformation.ipynb: Detect lines using the Hough Line Transformation.
• InRange Operation.ipynb: Apply an inRange operation on images.
• Installing Software and Files.ipynb: Guide on setting up necessary software and files.
• K-means Clustering.ipynb: Implement K-means clustering for image segmentation.
• Largest Contour Detection.ipynb: Find and work with the largest contour in an image.
• Morphological Black-Hat.ipynb: Apply morphological black-hat transformation.
• Morphological Closing.ipynb: Perform morphological closing operations.
• Morphological Dilation.ipynb: Implement morphological dilation.
• Morphological Erosion.ipynb: Use morphological erosion for image processing.
• Morphological Opening.ipynb: Perform morphological opening operations.
• Morphological Tophat.ipynb: Apply morphological top-hat transformation.
• Reading and Writing Image.ipynb: Guide to reading and writing images.
• Rectangular Binary Mask.ipynb: Create binary masks in rectangular shapes.
• Richardson-Lucy Algorithm Noise Remove.ipynb: Use the Richardson-Lucy algorithm for noise removal.
• Histogram Calculation: Folder with initial histogram calculation scripts.
• Morphological Gradient.ipynb: Calculate morphological gradients.
• Morphological Snake: Folder with initial morphological snake scripts.
• Skimage Thresholding: Folder containing scripts for skimage thresholding.

📖 Published Papers

Explore some of my published papers related to image processing and deep learning:

1. A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

   • Link: Read Paper
   • Cite:

     @ARTICLE{10113630,
       author={Raiaan, Mohaimenul Azam Khan and Fatema, Kaniz and Khan, Inam Ullah and Azam, Sami and Rashid, Md. Rafi Ur and Mukta, Md. Saddam Hossain and Jonkman, Mirjam and De Boer, Friso},
       journal={IEEE Access},
       title={A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images},
       year={2023},
       volume={11},
       number={},
       pages={42361-42388},
       doi={10.1109/ACCESS.2023.3272228}
     }

2. A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time

   • Link: Read Paper
   • Cite:

     @article{khan2023computer,
       title={A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time},
       author={Khan, Inam Ullah and Raiaan, Mohaimenul Azam Khan and Fatema, Kaniz and Azam, Sami and Rashid, Rafi ur and Mukta, Saddam Hossain and Jonkman, Mirjam and De Boer, Friso},
       journal={Biomedicines},
       volume={11},
       number={6},
       pages={1566},
       year={2023},
       publisher={MDPI},
       doi={10.3390/biomedicines11061566}
     }

3. SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset

   • Link: Read Paper
   • Cite:

     @INPROCEEDINGS{10101527,
       author={Fahad, Nur Mohammad and Sakib, Sadman and Khan Raiaan, Mohaimenul Azam and Hossain Mukta, Md. Saddam},
       booktitle={2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)},
       title={SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset},
       year={2023},
       volume={},
       number={},
       pages={1-6},
       doi={10.1109/ECCE57851.2023.10101527}
     }

📫 How to Reach Me

For any questions, concerns, or collaboration opportunities, feel free to reach out via email: 📫 m.raiaan.cs@gmail.com

🚀 Getting Started

Feel free to explore the notebooks and scripts based on your specific image preprocessing needs. You can click on the file names above to access each script or notebook.

Happy image preprocessing! If you have any questions or suggestions, feel free to open an issue or contribute to this repository.

👨‍💻 Happy Coding! 👩‍💻