Defence: IMPACT OF IMAGE PRE-PROCESSING METHODS ON COMPUTED TOMOGRAPHY RADIOMICS FEATURES IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Supervisor: Dr. Miranda Kirby

Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess
chronic obstructive pulmonary disease (COPD). However, different image pre-processing
methods are commonly used, and how these different methods impact radiomics features and lung
disease assessment, is unknown. Here, an image pre-processing pipeline was developed to
investigate how various pre-processing combinations impact radiomics features and their use for
COPD assessment. The pipeline included lung segmentation, airway segmentation or no
segmentation, image resampling or no resampling, and either no pre-processing, binning,
edgmentation, or thresholding pre-processing techniques. Image resampling and the different preprocessing techniques had the greatest effect on radiomics features. Features generated using the
resampling/edgmentation and resampling/thresholding combinations, regardless of airway
segmentation, performed the best in COPD classification, and explained the most variance with
lung function (R2≥0.353). Therefore, the image pre-processing methods completed prior to CT
radiomics feature extraction significantly impacted extracted features and their ability to assess
COPD.