V-CoHOG: Volumetric Co-occurrence Histograms of Oriented Gradients for Texture Classification in Medical Imaging

• Author / Creator

  Filbert, Alexander

• Image textures, which are properties that can describe the pixel intensities of an image,
  have been analyzed in order to perform a variety of tasks, including segmentation
  and classification. Features derived from textures which are invariant to changes such
  as contrast and transformations are of particular interest, due to their comparability
  across different images with related content. Hence, even early methods such as
  Gray-Level Co-occurrence Matrices (GLCM) have sought to describe textures using
  invariant features which proved to be effective in classification experiments. With the
  development of gradient based methods such as Histograms of Oriented Gradients
  (HOG) and derivatives including Co-occurrence Histograms of Oriented Gradients
  (CoHOG), an increasing variety of texture analysis methods have been applied to
  various domains, including medical imaging. When tasked with the classification
  of medical patients and healthy controls, methods such as Modified Co-occurrence
  Histograms of Oriented Gradients (M-CoHOG), which was applied to datasets of
  the neurodegenerative disorder Amyotrophic Lateral Sclerosis (ALS), have attained
  leading performance metrics. However, in the case of ALS, which can be difficult to
  diagnose, the lack of known biological imaging markers which could streamline the
  selection process of a region of interest (ROI), necessitates the contribution of an expert to segment the region of interest for M-CoHOG. Additionally, the consideration
  of only 2D imaging slices results in features with decreased spatial descriptiveness
  compared to volumetric approaches. In this thesis, a volumetric feature extraction
  method called V-CoHOG, extended on M-CoHOG, with automated ROI extraction
  is proposed. To reduce the feature vector size resulting from V-CoHOG, a feature selection method based on ReliefF is applied before classification using an ensemble
  model classifier with 5 base classifiers. The Canadian ALS Neuroimaging Consortium
  1 (CALSNIC-1) and, for the first time, CALSNIC-2 ALS datasets are used
  for evaluation and comparison with four 3-dimensional convolutional neural network
  (CNN) methods, with the additional Alzheimer’s Disease Neuroimaging Initiative
  (ADNI) Alzheimer’s disease dataset used to demonstrate the versatility of the proposed method. Analysis of the results demonstrates that the proposed method is
  able to consistently outperform the 3D CNN approaches on ALS datasets, while approaching and sometimes outperforming M-CoHOG performance on the CALSNIC-1
  datasets. Furthermore, a CUDA-accelerated implementation of M-CoHOG with significantly improved runtime performance on higher-resolution images is proposed.
  Finally, segmentation maps of selected features for ALS classification are overlaid
  onto the original imaging volumes. These maps allowed for the investigation of potential
  ALS imaging biomarkers, specifically their localization to brain regions present
  in the ROI.

• Subjects / Keywords

  • Computer Vision
  • Texture Classification
  • ALS
  • MRI

• Graduation date

  Fall 2022

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-3ygd-2n19

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.