A novel binary feature descriptor to discriminate normal and abnormal chest CT images using dissimilarity measures

In this paper, a new feature descriptor local diagonal Laplacian pattern (LDLP) is proposed to separate the normal and emphysematous lesions in computed tomographic (CT) images containing pulmonary emphysema. LDLP employs a diagonal element approach in which the relationship of the centre pixel with the diagonal elements is obtained using the second-order derivatives (Laplacian). This results in a low-dimensional feature vector with richer information about the local structure. In the proposed framework, the feature histograms of chest CT image slices of EMPHYSEMA database are first determined. Further, the distance between the features of normal and emphysematous tissues is measured and analysed using ANOVA statistical method. Furthermore, a four-class classification is performed using an artificial neural network classifier. The classification performance of the proposed LDLP approach is compared with the prominent methods like local binary pattern, local tetra pattern, and local diagonal extrema pattern. The observational results show that the LDLP outperforms the existing binary feature descriptors in the segregation and classification of healthy and abnormal chest CT images.