Enhanced local binary pattern for chest X-ray classification

Abstract

The Local Binary Pattern (LBP) descriptor encodes the complementary information of the spatial patterns and intensity variations in a local image neighborhood. The richness of this multidimensional information offers many possible variations to the encoding process. Taking advantage of this, several variants of the LBP have been proposed. This work attempts to further optimize the discriminative power of the LBP specifically for the medical image classification task. It proposes an LBP variant that takes into account the salient edge features that are found particularly in chest X-ray images. In addition, it introduces a semi-global histogram to replace the commonly used global histogram which normally represents the spatial distribution of the generated codes in the LBP encoding process. The proposed LBP variant has been applied to the task of classifying X-rays of the ImageCLEFmed 2009 dataset into different chest categories. Experimental results show that while reducing the computational load the proposed LBP variant achieved an accuracy of 99.19% as compared to the best reported LBP based results of 98.73%, in classifying chest x-ray images of the ImageCLEFmed 2009 dataset.

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

“…Literature also claimed of using an encoding technique of local binary pattern for classification. [36] The work of Mao et al [37] has used deep learning approach to perform classification of the lung nodule. The outcome shows better performance in contrast to other conventional classifiers.…”