Computer Vision Assignment: Image Segmentation Analysis

This repository contains the implementation and analysis of various image segmentation techniques applied to medical imaging data.

Project Structure

CV_Assignment/
├── data/           # Raw data files
├── docs/           # Documentation and PDF files
├── images/         # Output images and results

Overview

This project implements and compares different image segmentation techniques for medical image analysis, specifically focusing on brain MRI data. The implementation includes various methods such as:

• 2D and 3D Otsu Thresholding
• K-means Clustering
• Different Filtering Techniques

Results

3D Otsu Thresholding

Visualization of 3D Otsu thresholding results showing improved segmentation quality

Comparison of Different Techniques

Comparison of different segmentation approaches

Filtering Techniques

Analysis of various filtering techniques applied to the medical images

K-means Clustering

Results of K-means clustering segmentation

Implementation Details

The main implementation is contained in the Jupyter notebooks, which include:

• Data loading and preprocessing
• Implementation of various segmentation algorithms
• Comparative analysis of different methods
• Visualization of results

Data

The project uses brain MRI data (Brain.mat) for analysis and testing of the segmentation algorithms.

Documentation

Detailed documentation and the original assignment document can be found in the docs/ directory.

Key Findings

1. 3D Otsu thresholding showed improved results compared to traditional 2D approaches
2. K-means clustering provided effective segmentation with proper parameter tuning
3. Different filtering techniques showed varying levels of effectiveness in preprocessing

Usage

To run the analysis:

1. Open the notebooks in the notebooks/ directory
2. Ensure all dependencies are installed
3. Run the cells in sequence to reproduce the results

The final results and visualizations will be saved in the images/ directory.