Texture Classification of MR Images of the Brain in ALS using CoHOG

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.
Comment: We found an error in the feature selection part (Sec. 3.3) of the proposed approach. In the feature selection part, by mistake, we have used both the training and testing data for feature selection. We are working on it. We will update the proposed approach as early as possible