Lung Cancer Image Analysis and Feature Extraction in MATLAB

This project focuses on analyzing lung cancer images using MATLAB to extract meaningful information about lesions. We employ computer vision algorithms to automatically segment and classify pulmonary images through systematic image processing workflows. The implementation involves using MATLAB's Image Processing Toolbox functions such as imsegkmeans for clustering-based segmentation and regionprops for quantitative feature extraction from detected regions. We integrate these image-derived features with electronic health records from multiple patients using MATLAB's data integration capabilities, employing statistical analysis and machine learning techniques to identify potential risk factors and diagnostic markers. The project utilizes classification algorithms like support vector machines (implemented via fitcsvm) and clustering methods to correlate imaging biomarkers with clinical outcomes. Through these technical approaches, we aim to develop more accurate tools and methodologies for early lung cancer detection and treatment planning, contributing to computer-aided diagnosis systems.