Automatic Differentiation of u- and n-serrated Patterns in Direct Immunofluorescence Images

Abstract

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.

Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters

“…For subset 1, the recognition rate of u-serrated patterns is 84.6%, which is higher than the average recognition rate of 78.6% by trained medical doctors and pathologists [6] who perform the test on the same set. The proposed method performs significantly better than the other existing approach [13].…”

“…Here we explain how we detect the BMZ region before applying the proposed ridge-ending detectors. We use the algorithm proposed in [13] to segment the BMZ from the green channel (Fig. 10(b)) of a RGB DIF image (of size 1392 × 1040 pixels).…”

“…So far, there are only a few automatic techniques for serration pattern analysis in DIF images. The work proposed in [13] attempted to use as a feature vector the histogram of absolute orientations of ridges to automatically differentiate between n-and u-serrated DIF images. Later, the authors improved the method by considering the local orientation of the BMZ and constructed histograms of relative orientations of ridges with respect to the BMZ [14].…”

“…The work proposed in [13] attempted to use as a feature vector the histogram of absolute orientations of ridges to automatically differentiate between n-and u-serrated DIF images. Later, the authors improved the method by considering the local orientation of the BMZ and constructed histograms of relative orientations of ridges with respect to the BMZ [14]. Both methods, however, are unable to provide location information of u-serrated patterns in DIF images.…”

Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition

“…A COSFIRE filter is configured to be selective for a given local pattern by extracting from that pattern characteristic properties of contour parts (such as orientation) and their geometrical arrangement. COSFIRE filters were demonstrated to be effective for detection of local patterns (keypoints) and recognition of objects and achieve very good performance in various applications [4,6,8,19,47,49,50]. They were also used in a multilayer hierarchical approach [6].…”