MATLAB-based biomedical image processing workflow for brain tumor analysis from MRI data
This repository presents a MATLAB-based workflow for brain tumor detection and analysis using medical image processing techniques.
The project focuses on the core stages of a classical biomedical imaging pipeline:
- Image preprocessing
- Tumor-related segmentation
- Mask and region transfer operations
- Feature extraction
- Preparation of extracted data for WEKA-based classification
It is designed as both a technical project repository and a practical study resource for biomedical signal and image processing.
The repository contains MATLAB code for preprocessing and segmentation, along with feature extraction scripts and a project report. The repository description also indicates that the extracted feature .csv files are intended for use in WEKA for the next classification stage.
This makes the project a complete bridge between:
- Medical image enhancement and region processing in MATLAB
- Feature engineering
- Traditional machine learning classification in WEKA
Brain tumor analysis from MRI images is a highly relevant biomedical engineering problem.
This project demonstrates a structured workflow for tumor-focused image analysis by combining:
- MATLAB-based image processing
- region/mask operations
- interpretable feature extraction
- downstream machine learning preparation
It is especially valuable for students and engineers interested in:
- biomedical engineering
- medical image processing
- tumor segmentation workflows
- feature-based classification pipelines
- MATLAB implementation of healthcare-focused algorithms
.
├── Biomedical_Project_Report.pdf # Full report and technical documentation
├── feature_extraction_code.m # Feature extraction from processed/segmented regions
├── mask_transfer_code.m # Mask transfer operations
├── merge_image_code.m # Image merging / combined processing stage
├── segmenter_example.m # Segmentation example workflow
└── tumor_transfer_code.m # Tumor-region transfer / mapping operations
The project follows a staged biomedical image processing pipeline:
MRI images are processed in MATLAB to isolate relevant regions and prepare the data for tumor analysis.
Mask transfer and tumor-region transfer scripts are used to manipulate and preserve important tumor-related image regions.
Relevant numerical features are extracted from processed images and tumor-related regions.
The repository is designed so that extracted feature tables can be exported and used in WEKA for machine learning-based classification.
The project uses MATLAB as the main implementation platform for image-level operations and algorithm development.
The repository includes a segmentation example and multiple transfer-oriented scripts, suggesting an emphasis on isolating tumor-related structures before feature analysis.
A dedicated feature extraction script is included, indicating that the workflow does not stop at visualization or segmentation, but moves toward quantitative analysis.
The repository description explicitly notes that extracted feature .csv files are intended for WEKA in the next stage, making this project suitable for hybrid MATLAB + ML workflows.
- MATLAB-based biomedical image processing
- Tumor-oriented segmentation workflow
- Mask and region transfer operations
- Quantitative feature extraction
- WEKA-ready classification preparation
- Project report included for documentation and explanation
This project is a strong example of how classical image processing can support medical decision workflows.
It demonstrates:
- algorithmic structuring of biomedical image analysis
- practical MATLAB coding for healthcare applications
- transition from image domain processing to feature domain analysis
- interoperability between MATLAB and external machine learning tools
A typical workflow for using this repository would be:
- Load MRI data into MATLAB
- Run segmentation-related scripts
- Apply mask/tumor transfer operations
- Extract features from processed tumor regions
- Export the resulting data as
.csv - Use WEKA to build and evaluate classification models
Provides an example segmentation pipeline or initial tumor-region separation process.
Applies or transfers mask information between image representations or processing stages.
Transfers or isolates tumor-specific regions for subsequent analysis.
Combines intermediate outputs or integrates processed image layers.
Computes measurable features for later statistical or machine learning analysis.
This type of workflow can support:
- brain MRI analysis
- tumor-region isolation
- feature-based medical image classification
- educational biomedical engineering projects
- classical machine learning experiments on medical image features
- MATLAB
- MRI image dataset compatible with the scripts
- WEKA (optional, for classification stage)
- Open the MATLAB scripts in your MATLAB environment
- Review the sequence of preprocessing / segmentation scripts
- Run the transfer and merge steps as needed
- Execute feature extraction
- Export extracted data to
.csv - Import feature tables into WEKA for classification experiments
This repository can be extended with several high-impact upgrades:
- add a dataset description section
- document input/output format for each script
- include sample MRI images and output screenshots
- add evaluation metrics such as accuracy, sensitivity, specificity, and Dice score
- provide WEKA experiment settings and classifier comparisons
- add a reproducible end-to-end demo pipeline
- include a confusion matrix and classification results section
This repository is especially useful for learners and researchers interested in:
- MATLAB for medical imaging
- biomedical project development
- handcrafted feature extraction
- segmentation-based tumor analysis
- combining image processing with machine learning
It can be used as a foundation for:
- undergraduate biomedical projects
- medical imaging coursework
- feature-based ML studies
- early-stage tumor detection research prototypes
The repository includes a project report:
Biomedical_Project_Report.pdf
This should be the first reference point for understanding the project background, methodology, and implementation decisions.
Oğuzhan Alasulu
- GitHub: @alasulu
@misc{alasulu_brain_tumor_matlab,
author = {Oğuzhan Alasulu},
title = {Advanced Brain Tumor Detection Algorithm Utilizing MATLAB Project},
year = {2026},
howpublished = {\url{https://github.com/alasulu/Advanced-Brain-Tumor-Detection-Algorithm-Utilizing-MATLAB-Project}}
}This project reflects a classical but highly instructive biomedical imaging pipeline: process the MRI, isolate the suspicious region, extract meaningful features, and prepare the data for machine learning-driven tumor analysis.