Objective: This paper focuses on the use of image-based machine learning techniques in medical image analysis. In particular, we present some variants of local binary patterns (LBP), which are widely considered the state of the art among texture descriptors. After we provide a detailed review of the literature about existing LBP variants and discuss the most salient approaches, along with their pros and cons, we report new experiments using several LBP-based descriptors and propose a set of novel texture descriptors for the representation of biomedical images. The standard LBP operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Our variants are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. These sets of features are then used for training a machine-learning classifier (a stand-alone support vector machine). Methods and materials: Extensive experiments are conducted using the following three datasets: A database of neonatal facial images for classifying pain states starting from facial features. The 2D-HeLa dataset for cell phenotype image classification starting from fluorescent microscope images. Pap smear datasets for detecting abnormal smear cells. Results and conclusion: Our results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets. EQP is based on an elliptic neighborhood and a 5 levels scale for encoding the local gray-scale difference. Particularly interesting are the results on the widely studied 2D-HeLa dataset, where, to the best of our knowledge, the proposed descriptor obtains the highest performance among all the several texture descriptors tested in the literature. (C) 2010 Elsevier B.V. All rights reserved.
