Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non-uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co-occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub-region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non-uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co-occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub-region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state-of-the-art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy.