Abstract #1850
3D Texture Analysis of Heterogeneous MRI Data for the Characterisation of Childhood Brain Tumours
Ahmed E Fetit 1,2 , Jan Novak 2,3 , Daniel Rodriguez 4 , Dorothee P Auer 4,5 , Chris A Clark 6 , Richard G Grundy 4,5 , Tim Jaspan 5 , Andrew C Peet 2,3 , and Theodoros N Arvanitis 1,2
1
Institute of Digital Healthcare, WMG,
University of Warwick, Coventry, United Kingdom,
2
Birmingham
Children's Hospital, Birmingham, West Midlands, United
Kingdom,
3
University of Birmingham,
Birmingham, West Midlands, United Kingdom,
4
University
of Nottingham, Nottingham, United Kingdom,
5
University
Hospital Nottingham, Nottingham, United Kingdom,
6
University
College London, London, United Kingdom
There is an increasing interest in developing
quantitative MR image analysis tools that can capture
information below human visual perception and hence
assist the diagnosis of childhood brain tumours. In this
work, we compare the performance of 3D and 2D texture
analysis on multi-modal, heterogeneous MR data sets of
children diagnosed with Medulloblastoma, Pilocytic
Astrocytoma and Ependymoma. Additionally, we address the
problem of class imbalance by creating synthetic tumour
samples using Synthetic Minority Over-Sampling Technique
(SMOTE) through operating in feature space. Our results
support the use of texture analysis as an automated,
quantitative technique to assist with the diagnosis of
paediatric brain tumours.
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