HEp-2 Cell Classification: The Role of Gaussian Scale Space Theory as A Pre-processing Approach

TL;DR

This paper demonstrates that applying Gaussian Scale Space pre-processing significantly improves HEp-2 cell classification accuracy, outperforming existing methods and even the combined-feature winner of a major contest.

Contribution

The study introduces Gaussian Scale Space as an effective pre-processing step for HEp-2 cell classification, enhancing performance with minimal features.

Findings

GSS pre-processing improves classification accuracy

Single LOAD feature outperforms multi-feature approaches

Method outperforms ICPR 2014 contest winner

Abstract

\textit{Indirect Immunofluorescence Imaging of Human Epithelial Type 2} (HEp-2) cells is an effective way to identify the presence of Anti-Nuclear Antibody (ANA). Most existing works on HEp-2 cell classification mainly focus on feature extraction, feature encoding and classifier design. Very few efforts have been devoted to study the importance of the pre-processing techniques. In this paper, we analyze the importance of the pre-processing, and investigate the role of Gaussian Scale Space (GSS) theory as a pre-processing approach for the HEp-2 cell classification task. We validate the GSS pre-processing under the Local Binary Pattern (LBP) and the Bag-of-Words (BoW) frameworks. Under the BoW framework, the introduced pre-processing approach, using only one Local Orientation Adaptive Descriptor (LOAD), achieved superior performance on the Executable Thematic on Pattern Recognition Techniques for Indirect Immunofluorescence (ET-PRT-IIF) image analysis. Our system, using only one feature, outperformed the winner of the ICPR 2014 contest that combined four types of features. Meanwhile, the proposed pre-processing method is not restricted to this work; it can be generalized to many existing works.