Meeting Banner
Abstract #3499

MR Intensity Normalization: Influence on Supervised Machine Learning Algorithms using Textural and Convolutional Features

Mariana Bento1, Marina Salluzzi2, Leticia Rittner3, and Richard Frayne1

1Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 2Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada, 3School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil

Supervised machine learning techniques have been used in medical imaging and aim to reduce subjectivity and improve quantitative results. When handling heterogeneous MR datasets, most algorithms require pre-processing, such as intensity normalization. Here, the influence of MR normalization techniques on supervised classification is evaluated using handcrafted textural and convolutional features. These features combined can differentiate control subjects from atherosclerosis patients using only imaging scans. Non-significant statistical difference in classification was found across intensity normalization methods, demonstrating little influence of this pre-processing step on the supervised classification outcome.

This abstract and the presentation materials are available to members only; a login is required.

Join Here