A vector representation of local image contrast patterns for lesion classification

Quantitative description of lesion image heterogeneity is a major task for computer-aided diagnosis of lesions, and it has remained a challenging task because the heterogeneity is associated with local image contrast patterns of each voxel. This work explores a novel vector representation of the local image contrast patterns of each voxel and learns the features from the local vector field across all voxels in the lesion volume. We generate a matrix from the first ring of surrounding voxels from each voxel in the image and perform a Karhunen-Loève transformation on this matrix. Using the eigenvectors associated with the largest three eigenvalues, we then generate a series of textures based on a vector representation of this matrix. Using an in-house dataset, experiments were performed to classify colorectal polyps using the learnt features and a Random Forest classifier to differentiate malignant from benign lesions. The outcomes show dramatic improvement for the lesion classification compared to seven existing classification methods (e.g. LBP, Haralick, VGG16), which learn the features from the original intensity image.