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https://github.com/pasanlaksitha/medimageanalyser
A MATLAB-based application designed to segment and identify parasite features in medical images using advanced image processing techniques. The application incorporates functionalities such as grayscale conversion, histogram generation, thresholding, and morphological filtering, tailored for Katokatz images.
https://github.com/pasanlaksitha/medimageanalyser
image-analyzer image-processing matlab westminster
Last synced: 12 days ago
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A MATLAB-based application designed to segment and identify parasite features in medical images using advanced image processing techniques. The application incorporates functionalities such as grayscale conversion, histogram generation, thresholding, and morphological filtering, tailored for Katokatz images.
- Host: GitHub
- URL: https://github.com/pasanlaksitha/medimageanalyser
- Owner: Pasanlaksitha
- License: mit
- Created: 2025-01-06T06:59:31.000Z (16 days ago)
- Default Branch: main
- Last Pushed: 2025-01-06T07:03:33.000Z (16 days ago)
- Last Synced: 2025-01-06T08:18:47.609Z (16 days ago)
- Topics: image-analyzer, image-processing, matlab, westminster
- Homepage:
- Size: 2.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MedImageAnalyser
## Overview
MedImageAnalyser is a MATLAB-based graphical user interface (GUI) application developed for medical image processing. The primary focus is on analyzing and enhancing Katokatz images to segment and identify parasite features. This project is part of the academic coursework for the Sensors and Signals module (5ELEN021W).## Features
- Upload and display medical images.
- Calculate RMS contrast for images.
- Convert images to grayscale using optimized RGB weights.
- Generate and display histograms for grayscale images.
- Perform image thresholding and binarization.
- Apply morphological operations with custom structuring elements.## Objectives
- Efficient segmentation of parasite features in medical images.
- Noise reduction and background smoothing using morphological filtering.
- Feature enhancement and segmentation through preprocessing techniques.## Dataset
The application uses images from the **ADVRG database** provided as part of the coursework.## GUI Features
- **Upload Images**: Load Katokatz images into the application.
- **Contrast Calculation**: Compute RMS contrast to measure image quality.
- **Grayscale Conversion**: Convert images to grayscale using weighted RGB channels.
- **Histogram Display**: Generate and view the intensity distribution of grayscale images.
- **Thresholding and Morphological Filtering**: Enhance image features and remove noise.System Requirements
MATLAB R2024b or later.
Image Processing Toolbox (recommended).
Screenshots
Main GUI InterfaceExample Outputs
Grayscale Conversion:Histogram Display:
Morphological Filtering:
![image](https://github.com/user-attachments/assets/906e48bc-e0a3-49d4-bc8a-fb7b5fedea64)
## Acknowledgments
This project is developed as part of the coursework for Sensors and Signals (5ELEN021W) at the Informatics Institute of Technology, affiliated with the University of Westminster, under the guidance of **Hasini Punsara Kasthuriarachchi**.