Version 1
: Received: 30 October 2017 / Approved: 31 October 2017 / Online: 31 October 2017 (03:10:22 CET)
How to cite:
Mukundan, R. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. Preprints2017, 2017100187. https://doi.org/10.20944/preprints201710.0187.v1
Mukundan, R. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. Preprints 2017, 2017100187. https://doi.org/10.20944/preprints201710.0187.v1
Mukundan, R. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. Preprints2017, 2017100187. https://doi.org/10.20944/preprints201710.0187.v1
APA Style
Mukundan, R. (2017). Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. Preprints. https://doi.org/10.20944/preprints201710.0187.v1
Chicago/Turabian Style
Mukundan, R. 2017 "Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring" Preprints. https://doi.org/10.20944/preprints201710.0187.v1
Abstract
This paper presents novel feature descriptors and classification algorithms for 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 an 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 comparative analysis.
Keywords
medical image classification; local binary patterns; characteristic curves; whole slide image pro-cessing; automated HER2 scoring
Subject
Computer Science and Mathematics, Analysis
Copyright:
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.
