Overview

This project focuses on substructure detection and segmentation in heterogeneous 3D datasets, including volumetric reconstructions and point-cloud representations derived from different experimental techniques.

The primary objective is to develop a generalizable and modular framework capable of identifying hidden structural patterns without relying on supervised labels or domain-specific tuning.

Problem Statement

Modern experimental datasets often exhibit:
- high dimensionality and large data volumes,
- heterogeneous data representations (voxels, point clouds, grids),
- noise, artifacts, and uneven sampling density.

These factors make traditional rule-based or supervised segmentation approaches difficult to apply consistently across datasets.

Approach

The developed framework:
- Ingests multiple 3D data formats and representations
- Performs preprocessing and normalization tailored to each data type
- Extracts geometric and statistical features from the data
- Applies unsupervised clustering and density-based segmentation methods
- Evaluates segmentation quality using data-driven criteria

The pipeline is designed to be modular, allowing rapid adaptation to new datasets and experimental conditions.

Methodology

Key methodological components include:
- Point-cloud and voxel-based data handling
- Feature extraction from local neighborhoods
- Density estimation and clustering for substructure separation
- Visualization of segmented structures in 3D space

This approach enables discovery of previously hidden substructures without imposing strong model assumptions.

Technologies Used

• Python
• NumPy, SciPy
• scikit-learn (clustering and unsupervised methods)
• 3D data handling and visualization libraries
• Jupyter Notebook / modular Python scripts

Results

• Successful segmentation of complex 3D datasets across multiple data types
• Identification of distinct substructures in noisy and heterogeneous data
• Demonstrated robustness of unsupervised methods for real experimental data

Applications

• 3D tomographic data analysis
• Biological and materials science imaging
• Point-cloud segmentation and structural analysis
• Exploratory analysis of large-scale experimental datasets

How to Run

1. Clone the repository:
   bash git clone https://github.com/mohdrafik/Substructure_Different_DataTypes