Classical Image Segmentation

This repository contains a collection of image segmentation algorithms implemented in Python using OpenCV, SimpleITK, and NumPy.

Segmentation Algorithms

The following segmentation algorithms are included:

1. Fast Marching Segmentation: Implemented using SimpleITK.
2. Threshold Segmentation: Implemented using OpenCV.
3. Canny Edge Detection: Implemented using OpenCV.
4. Contour Detection: Implemented using OpenCV.
5. K-means Segmentation: Implemented using OpenCV.
6. Watershed Segmentation: Implemented using OpenCV.

Each algorithm is implemented in a separate Python file for better organization.

Usage

To use these segmentation algorithms, follow these steps:

1. Clone this repository to your local machine:

git clone https://github.com/SYED-M-HUSSAIN/Classical-Image-Segmentation-On-Microorganisms.git

2. Install the required dependencies:

  pip install -r requirements.txt

3. Run the main file:

  python3 main.py

Results Visualization

Here are the segmented images produced by each algorithm on test folder images:

These segmentation algorithms can be used for various applications, including medical image analysis, object detection, and image processing tasks. Contributions are welcome! If you have any ideas for improvement or new segmentation algorithms to add, feel free to open an issue or submit a pull request.

References

OpenCV and SimpleITK