Coccurrence Statistics of Local Ternary Patterns for HEp-2 Cell Classification

In this paper, we describe a novel image representation strategy for classifying HEp-2 cell patterns of fluorescence staining. Our proposed strategy extends local binary patterns (LBPs), which are state-of-the-art texture features, into local ternary patterns (LTPs) with data-driven thresholds according to Weber's law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences among adjacent Weber-based local ternary patterns (WLTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating spatial and orientation context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. Through experiment on the open HEp-2 cell dataset used at the ICIP2013 contest, we confirmed that our proposed strategy can greatly improve recognition performance or achieve comparable performance as compared with state-of-the-art LBP-based descriptor, the conventional LTP, and adaptively codebook/model based methods.