Abstract
The evaluation of breast cancer grades in immunohistochemistry (IHC) slides takes into account various types of visual markers and morphological features of stained membrane regions. Digital pathology algorithms using whole slide images (WSIs) of histology slides have recently been finding several applications in such computer-assisted evaluations. Features that are directly related to biomarkers used by pathologists are generally preferred over the pixel values of entire images, even though the latter has more information content. This paper explores in detail various types of feature measurements that are suitable for the automated scoring of human epidermal growth factor receptor 2 (HER2) in histology slides. These are intensity features known as characteristic curves, texture features in the form of uniform local binary patterns (ULBPs), morphological features specifying connectivity of regions, and first-order statistical features of the overall intensity distribution. This paper considers important properties of the above features and outlines methods for reducing information redundancy, maximizing inter-class separability, and improving classification accuracy in the combined feature set. This paper also presents a detailed experimental analysis performed using the aforementioned features on a WSI dataset of IHC stained slides.
Highlights
Whole slide images (WSIs) of entire biopsy slides facilitate the processing of a wide range of features extracted from regions of interest for applications such as automated scoring of the tumour characteristics seen in the images [1]
This paper proposed a set of image features that are closely related
support vector machine (SVM) to visual markers used for Entropy slides
Summary
Whole slide images (WSIs) of entire biopsy slides facilitate the processing of a wide range of features extracted from regions of interest for applications such as automated scoring of the tumour characteristics seen in the images [1]. In the past few years, advances in WSI instrumentation have provided the ability to automatically load up to 300 slides without user intervention, with considerably faster scanning speeds [5] This has resulted in the need for fast image processing algorithms that can detect and analyse various image and texture characteristics, and accurately extract cytological and morphological features that are relevant to. Our previous work [21] used Fisher linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the effectiveness of the features, reduce feature dimension, combine features of different types, maximize inter-class separability and to improve the overall classification accuracy Such discriminant analysis approaches are commonly used to study the feature transformations used in multi-class classification algorithms [22,23].
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