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J. Imaging 2019, 5(3), 35; https://doi.org/10.3390/jimaging5030035

Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8140, New Zealand
This paper is an extended version of our paper published in the 22nd Conference on Medical Image Understanding and Analysis, Southampton, UK, 9–11 July 2018.
Received: 16 December 2018 / Revised: 1 March 2019 / Accepted: 6 March 2019 / Published: 10 March 2019
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
Full-Text   |   PDF [3680 KB, uploaded 10 March 2019]   |  

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. View Full-Text
Keywords: uniform local binary patterns; characteristic curves; whole slide image processing; feature analysis; region connectivity; entropy uniform local binary patterns; characteristic curves; whole slide image processing; feature analysis; region connectivity; entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mukundan, R. Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. J. Imaging 2019, 5, 35.

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