  Texture analysis is a well-known research topic in computer vision and image
processing and has many applications. Gradient-based texture methods have
become popular in classification problems. For the first time we extend a
well-known gradient-based method, Co-occurrence Histograms of Oriented
Gradients (CoHOG) to extract texture features from 2D Magnetic Resonance Images
(MRI). Unlike the original CoHOG method, we use the whole image instead of
sub-regions for feature calculation. Also, we use a larger neighborhood size.
Gradient orientations of the image pixels are calculated using Sobel, Gaussian
Derivative (GD) and Local Frequency Descriptor Gradient (LFDG) operators. The
extracted feature vector size is very large and classification using a large
number of similar features does not provide the best results. In our proposed
method, for the first time to our best knowledge, only a minimum number of
significant features are selected using area under the receiver operator
characteristic (ROC) curve (AUC) thresholds with <= 0.01. In this paper, we
apply the proposed method to classify Amyotrophic Lateral Sclerosis (ALS)
patients from the controls. It is observed that selected texture features from
downsampled images are significantly different between patients and controls.
These features are used in a linear support vector machine (SVM) classifier to
determine the classification accuracy. Optimal sensitivity and specificity are
also calculated. Three different cohort datasets are used in the experiments.
The performance of the proposed method using three gradient operators and two
different neighborhood sizes is analyzed. Region based analysis is performed to
demonstrate that significant changes between patients and controls are limited
to the motor cortex.
