This repository contains two instructional MATLAB projects that demonstrate how classical digital image processing can be used to extract quantitative information from materials- and geology-related images.
The projects were developed for a course in Digital Image Processing and focus on real engineering tasks:
- Fracture Detection in Rocks using Digital Image Processing
- Analysis of Ooid Iron in Ironstone
Both projects show the full chain: image acquisition → preprocessing → feature extraction → visualization. They can be adapted to metallographic/SEM images from LPBF or corrosion studies.
Source: original course projects in this repository. :contentReference[oaicite:2]{index=2}
-
Fracture Detection in Rocks using Digital Image Processing/
MATLAB scripts for:- converting to grayscale
- noise reduction (filtering)
- edge detection (Sobel/Canny)
- morphological cleaning (dilation/erosion)
- overlaying detected fractures on the original rock image
-
Analysis of Ooid Iron in Ironstone/
MATLAB scripts for:- contrast enhancement
- segmentation of ooids / iron-rich regions
- labeling objects
- measuring area / count / shape factors
- exporting basic statistics
-
README.md
(this file)
At the time of writing, the GitHub “About” section is empty; this README describes the actual scope. :contentReference[oaicite:3]{index=3}
- show classical (non–deep-learning) image processing in MATLAB,
- work with realistic, non-perfect material/rock images,
- produce reproducible analysis scripts students can modify,
- make it easy to plug in your own SEM / metallography / LPBF micrographs.
- MATLAB (R2021a or newer recommended)
- Image Processing Toolbox
- sample images (provided in the project folders or replace with your own PNG/JPG/TIF)
No GPU is needed — these are classic image-processing pipelines.
- Clone the repo:
git clone https://github.com/mengedagnaw/Image-processing-MATLAB.git cd Image-processing-MATLAB