This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging.
Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI.
Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features.
Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression.
On the image bellow you can see an attached worflow of thesis:
Big part of the thesis is destinated to visualize the most most descriptive features for distinguishing the class of the tumour. Images comes from 3DSlicer, GIMP or ImageJ
