An Integrated Multi-scale Model for Breast Cancer Histopathological Image Classification with Joint Colour-Texture Features

Breast cancer is one of the most commonly diagnosed cancer in women worldwide, and is commonly diagnosed via histopathological microscopy imaging. Image analysis techniques aid physicians by automating some tasks involved in the diagnostic workflow. In this paper, we propose an integrated model that considers images at different magnifications, for classification of breast cancer histopathological images. Unlike some existing methods which employ a small set of features and classifiers, the present work explores various joint colour-texture features and classifiers to compute scores for the input data. The scores at different magnifications are then integrated. The approach thus highlights suitable features and classifiers for each magnification. Furthermore, the overall performance is also evaluated using the area under the ROC curve (AUC) that can determine the system quality based on patient-level scores. We demonstrate that suitable feature-classifier combinations can largely outperform the state-of-the-art methods, and the integrated model achieves a more reliable performance in terms of AUC over those at individual magnifications.

[1]  Marek Kowal,et al.  Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images , 2013, Comput. Biol. Medicine.

[2]  Bernard R. Rosner,et al.  Fundamentals of Biostatistics. , 1992 .

[3]  M. Pietikäinen,et al.  TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS , 2004 .

[4]  Roman Monczak,et al.  Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies , 2013, IEEE Transactions on Medical Imaging.

[5]  Neeraj Kumar,et al.  Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images , 2016, Journal of pathology informatics.

[6]  Marcial García-Rojo,et al.  Influence of Texture and Colour in Breast TMA Classification , 2015, PloS one.

[7]  Juho Kannala,et al.  Deep learning for magnification independent breast cancer histopathology image classification , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[8]  Stephen J. McKenna,et al.  Classification of breast-tissue microarray spots using colour and local invariants , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  Frans Coenen,et al.  One-class kernel subspace ensemble for medical image classification , 2014, EURASIP Journal on Advances in Signal Processing.

[10]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[11]  Konstantinos N. Plataniotis,et al.  A Complete Color Normalization Approach to Histopathology Images Using Color Cues Computed From Saturation-Weighted Statistics , 2015, IEEE Transactions on Biomedical Engineering.

[12]  A. Tabesh,et al.  Tumor Classification in Histological Images of Prostate Using Color Texture , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[13]  Frans Coenen,et al.  Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles , 2012, Machine Vision and Applications.

[14]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[15]  Anindya Sarkar,et al.  Structure and Context in Prostatic Gland Segmentation and Classification , 2012, MICCAI.

[16]  Luiz Eduardo Soares de Oliveira,et al.  Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[17]  Aaron Fenster,et al.  Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification , 2013, IEEE Transactions on Medical Imaging.

[18]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[19]  Francesco Bianconi,et al.  Rotation-invariant colour texture classification through multilayer CCR , 2009, Pattern Recognit. Lett..

[20]  M. Spann,et al.  Colour-based texture image classification using the complex wavelet transform , 2008, 2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control.

[21]  Nozha Boujemaa,et al.  Color texture classification by normalized color space representation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[22]  Paul Southam,et al.  Theoretical and experimental comparison of different approaches for color texture classification , 2011, J. Electronic Imaging.

[23]  Paul F. Whelan,et al.  Image segmentation based on the integration of colour-texture descriptors - A review , 2011, Pattern Recognit..

[24]  Arnold W. M. Smeulders,et al.  Color texture measurement and segmentation , 2005, Signal Process..

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Gyan Bhanot,et al.  Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.

[27]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[28]  Russell Zaretzki,et al.  The Skill Plot: A Graphical Technique for Evaluating Continuous Diagnostic Tests , 2007, Biometrics.