Efficient detection and classification of brain tumors using a novel local binary pattern

Local binary pattern (LBP) is a computationally inexpensive feature descriptor popularly used for detecting and classifying images. However, the directional attributes associated with conventional LBP lead to the generation of undesirable shades and noisy textures. Consequently, it impairs the features corresponding to edges and intensity gradients in an image. To address this problem, in this article, we have proposed a novel LBP-Like (LBP-L) transform, which could be used as an efficient alternative to the conventional LBP. The LBP-L of an image does not change due to horizontal and vertical flipping of images or when the image is rotated through 90°, 180°, or 270°. Besides, it maps the uniform intensity zones and edges in images to the same feature space irrespective of the orientation. It has significant potential to boost the detection and classification of brain tumors in magnetic resonance (MR) images. The performance of LBP-L has been tested in two benchmark datasets having 3064 and 7023 MR images of the brain with tumors of three different types, namely, meningioma, glioma, and pituitary, along with images without tumors. The detection and classification accuracies of LBP-L on the Kaggle and Figshare datasets are higher than those of the conventional LBP and its variants reported in the literature. It is shown that LBP-L has computational complexity comparable to that of traditional LBP. Moreover, the proposed LBP-L is found to have a lower computation time compared to the well-known LBP variants. Therefore, it could be used as a more efficient substitute for the latter.