Local Binary Patterns for Automated HER2 Scoring

1 Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, 5 New Zealand; [email protected] 6 * Correspondence: [email protected]; Tel.: +64-3-3692201 7 8 Abstract: This paper presents novel feature descriptors and classification algorithms for 9 automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a 10 large amount of processing is involved in analyzing WSI images, the primary design goal has been 11 to keep the computational complexity to the minimum possible level and to use simple, yet robust 12 feature descriptors that can provide accurate classification of the slides. We propose two types of 13 feature descriptors that encode important information about staining patterns and the percentage of 14 staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a 15 characteristic curve which is a smooth non-increasing curve that represents the variation of 16 percentage of staining with saturation levels. The second new descriptor introduced in this paper is 17 an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of 18 the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, 19 and are suitable for the design of automatic HER2 classification algorithms. This paper gives the 20 detailed theoretical aspects of the feature descriptors and also provides experimental results and 21 comparative analysis. 22