Application of Magnetic Resonance Radiomics Platform (MRP) for Machine Learning Based Features Extraction from Brain Tumor Images

Abstract

This study investigated the implementation of magnetic resonance radiomics platform (MRP) for machine learning based features extraction from brain tumor images. Magnetic resonance imaging data publicly available in The Cancer Imaging Archive (TCIA) were downloaded and used to perform image Coregistration, Multi-Modality, Images interpolation, Morphology and Extraction of radiomic features with MRP tools. Radiomics analyses were then applied to the data (containing AX-T1-POST, Diffusion weighted, AX-T2-FSE and AX-T2-FLAIR sequences) using wavelet decomposition principles. The results employing different configurations of low-pass and high-pass filters were exported to Microsoft excel data sheets. The exported data were visualized using MATLAB’s classification learner tool. These exported data and the visualizations provide a new way of deep assessment of image data as well as easier interpretation of image scans. Findings from this study revealed that Machine learning Radiomics Platform is important in characterizing, visualizing and gives adequate information of a brain tumor.