Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images

Abstract

Local Binary Patterns (LBP) is a widely used approach for medical image analysis. Limitations of the LBP operator are its sensitivity to noise and its boundedness to first derivative information. These limitations are usually balanced by extensions of the classical LBP operator (e.g. the Local Ternary Pattern operator (LTP) or the Extended LBP (ELBP) operator). In this paper we present a generic framework that is able to overcome this limitations by frequency filtering the images as pre-processing stage to the classical LBP. The advantage of this approach is its easier adaption and optimization to different application scenarios and data sets as compared to other LBP variants. Experiments are carried out employing two endoscopic data sets, the first from the duodenum used for diagnosis of celiac disease, the second from the colon used for polyp malignity assessment. It turned out that high pass filtering combined with LBP outperforms classical LBP and most of its extensions, whereas low pass filtering effects the results only to a small extent.