V-CoHOG: Volumetric Co-occurrence Histograms of Oriented Gradients for Texture Classification in Medical Imaging
Creators: Filbert, Alexander
Publisher: name: University of Alberta Library
Year: 2022

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