Feature Analysis of Biomarker Descriptors for HER2 Classification of Histology Slides

Abstract

Whole slide images (WSI) of histology slides are increasingly being used for computer assisted evaluations, automated grading and classification. In this rapidly evolving research field, several classification algorithms and feature descriptors have been reported for histopathological analysis. While some algorithms use pixel values of entire images as features, other methods try to use specific biomarker related features. This paper analyses in detail feature descriptors that have been found to be efficient in classifying ImmunoHistoChemistry (IHC) stained slides. These features are directly related to the Human Epidermal Growth Factor Receptor 2 (HER2) biomarkers that are commonly used for grading such slides. Characteristic curves are intensity features that encode information about the variation of the percentage of stained membrane regions with saturation levels. The uniform Local Binary Patterns (ULBP) are texture features extracted from stained regions. ULBP contains several components and generates a high dimensional feature vector that needs to be compressed. Fisher Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to select feature components important in classification. The paper proposes a method to combine different types of features (e.g., intensity and texture) after dimensionality reduction, and to improve classification accuracy by maximizing inter-class separability. The paper also discusses methods to visualize class-wise distribution of the computed feature vectors. Experimental analysis performed using a WSI dataset of IHC stained slides and aforementioned features are also presented.