Automatic differentiation of u- and n-serrated patterns in direct immunofluorescence images

Abstract

Epidermolysis bullosa acquisita (EBA) is a subepidermal autoimmune blistering disease of the skin. Manual u- and n-serrated patterns analysis in direct immunofluorescence (DIF) images is used in medical practice to differentiate EBA from other forms of pemphigoid. The manual analysis of serration patterns in DIF images is very challenging, mainly due to noise and lack of training of the immunofluorescence (IF) microscopists. There are no automatic techniques to distinguish these two types of serration patterns. We propose an algorithm for the automatic recognition of such a disease. We first locate a region where u- and n-serrated patterns are typically found. Then, we apply a bank of B-COSFIRE filters to the identified region of interest in the DIF image in order to detect ridge contours. This is followed by the construction of a normalized histogram of orientations. Finally, we classify an image by using the nearest neighbors algorithm that compares its normalized histogram of orientations with all the images in the dataset. The best results that we achieve on the UMCG publicly available data set is 84.6% correct classification, which is comparable to the results of medical experts.