Statistical Local Binary Patterns (SLBP): Application to Prostate Cancer Gleason Score Prediction from Whole Slide Pathology Images

Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more micro- and macro-structure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade ≤7 vs grade ≥8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-art LBP variants.

[1]  Matti Pietikäinen,et al.  Median Robust Extended Local Binary Pattern for Texture Classification , 2016, IEEE Transactions on Image Processing.

[2]  Mrinal K. Mandal,et al.  Automated analysis and classification of melanocytic tumor on skin whole slide images , 2018, Comput. Medical Imaging Graph..

[3]  Adrien Depeursinge,et al.  Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles , 2016, Medical Image Anal..

[4]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[5]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[6]  Hongming Xu,et al.  Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm , 2015, EURASIP Journal on Image and Video Processing.

[7]  Francesco Bianconi,et al.  Multi-class texture analysis in colorectal cancer histology , 2016, Scientific Reports.

[8]  Jonathan Epstein,et al.  Grading of prostatic adenocarcinoma: current state and prognostic implications , 2016, Diagnostic Pathology.

[9]  Claus Bahlmann,et al.  Computer-aided gleason grading of prostate cancer histopathological images using texton forests , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  Mats Andersson,et al.  Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score , 2017, Medical Imaging.

[11]  Po-Whei Huang,et al.  Automatic Classification for Pathological Prostate Images Based on Fractal Analysis , 2009, IEEE Transactions on Medical Imaging.

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Sos S. Agaian,et al.  Gleason grade-based automatic classification of prostate cancer pathological images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.