In this project, we extract features namely blood vessels microaneurysms and exudates for the purpose of analysing fundus images to detect signs of retinal tissue damage.
For exudates segmentation, Please visit Retinal Exudates Detection.
These instructions will get you a copy of the project up and running on your local machine for you to test the algorithms on your own fundus images. The MESSIDOR database has been used for demonstration here for which we are grateful.
The main idea is to use ImageMagick's convert tool to trim off the blank space to the sides of the images, then pad them so that they are all 256x256. Thus the eye is always centered with edges against the edges of the image.
And also to create multiple versions of each image varying by hue and contrast and white balance.
You must have ImageMagick's convert tool and GNU Parallel installed in order to run this.
$ sudo apt install imagemagick
$ sudo apt install parallel
These are available in all the major linux repositories.
Run prep_image.sh on each image to prepare the image variations and resized images.
- NOTE: Make sure "Allow executing file as a program" is checked in the prep_image.sh Properties.
Assuming that your train and test images are in folders train/ and test/ respectively and the file prep_image.sh lies in your project's root directory, run the following command:
$ ls train/*.jpeg test/*.jpeg | parallel ./prep_image.sh
This will preprocess all the images into processed/<variation>/train/ and processed/<variation>/test/.
The following must be installed and configured:
Follow the links for source and installation instructions.
Change the pathFolder and destinationFolder variable in the file bloodvessels.py to point to the directory where your images are located.
pathFolder = "/home/sherlock/DR/MODEL_1_SVM/Base11/"
destinationFolder = "/home/sherlock/DR/MODEL_1_SVM/Base11Bloodvessels/"
Open a terminal and navigate to where the bloodvessels.py file is located and run the following command:
$ python bloodvessels.py
OR
$ python3 bloodvessels.py
For each image in your input folder, this will save one image <imageName>_Bloodvessel.png (segmented Bloodvessels).
For some image in Base11:
Following resultant image is generated in the Destination folder:
- Bloodvessels.png
The following must be installed and configured:
Follow the links for source and installation instructions.
Change the pathFolder variable in the file microaneurysm.py to point to the directory where your images are located.
pathFolder = "/home/utkarsh/SimpleCV/input"
- NOTE: Make sure you manually create a file
ma.csvin the same directory as that ofmicroaneurysm.py. Open a terminal and navigate to where themicroaneurysm.pyfile is located and run the following command:
$ python microaneurysm.py
OR
$ python3 microaneurysm.py
depending upon the version of python for which SimpleCV is configured.
For each image in your input folder, this will save two images <imageName>_MA.tif (detected aneurysms in white over black image) and <imageName>_MAoverlay.tif (detected aneurysms in white overlaid upon the original image).
Also, the file ma.csv will now contain the white pixel count from the <imageName>_MA.tif for each image in the input folder.
For a single image in the input folder /home/utkarsh/SimpleCV/input/input.tif:
Following two images are generated in the same folder as microaneurysm.py:
- input_MA.tif and input_MAoverlay.tif
- The
ma.csvfile looks like:
| microaneurysmcount | countvalue |
|---|---|
| input_microaneurysm.jpg | 2920 |
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
- Sanjeev Dubey - [email protected]
- Utkarsh Mittal - [email protected]
- Raghav Singh - [email protected]
See also the list of contributors who participated in this project.
This project is licensed under the BSD-3-Clause License - see the LICENSE.md file for details