Local gradient of gradient pattern: a robust image descriptor for the classification of brain strokes from computed tomography images

Abstract

This paper presents a new feature extraction method for the classification of brain computed tomography (CT) scan images into hemorrhagic strokes, ischemic strokes and normal CT images. The most popular feature extraction method is local binary pattern (LBP), which works by thresholding the neighboring pixel values with the center pixel value of the image. Unlike LBP, our proposed method is based on comparing neighbors of center pixel and the mean of whole image intensities in the first step, and computing double gradients of local neighborhoods of a center pixel of the original image in x and y directions in the second step. Further, values obtained from the first step are compared with double gradients of neighbors in order to generate codes for the center pixel. We have also calculated the codes for the first step. Thereafter, histograms of all the codes are generated and finally concatenated to form a single feature vector. We termed this descriptor as the local gradient of gradient pattern. We have performed nine different experiments where images have been classified using various classifiers. The efficacy of our feature descriptor for image classification is identified by comparing it with seven different feature extraction methods. Performances of these methods are tested using metrics such as precision, true positive rate, false positive rate, F-measure and accuracies of the classifier. Results obtained show that our method is superior to other previous descriptors.