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J. Imaging 2018, 4(2), 35; https://doi.org/10.3390/jimaging4020035

Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring

Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8140, New Zealand
This article is an extended version of our paper published in Mukundan, R. A Robust Algorithm for Automated Her2 Scoring in Breast Cancer Histology Slides Using Characteristic Curves. In Medical Image Understanding and Analysis; Springer: Cham, Switzerland, 2017; pp. 386–397.
Received: 30 October 2017 / Revised: 1 February 2018 / Accepted: 2 February 2018 / Published: 5 February 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

This paper presents novel feature descriptors and classification algorithms for the automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC)-stained slides. The first descriptor is called a characteristic curve, which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is a local binary pattern (LBP) feature curve, which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and a comparative analysis. View Full-Text
Keywords: medical image classification; local binary patterns; characteristic curves; whole slide image processing; automated HER2 scoring medical image classification; local binary patterns; characteristic curves; whole slide image processing; automated HER2 scoring
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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. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. J. Imaging 2018, 4, 35.

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