Automated classification for HEp-2 cells based on linear local distance coding framework

Abstract

The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers from serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells classification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image classification tasks because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture more discriminative information. Our LLDC method transforms original local feature to more discriminative local distance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC method is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with a linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public datasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International Conference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can achieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells.