  \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.
