Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach

• Published in: IEEE transactions on medical imaging Vol. 35; no. 1; pp. 307 - 315
• Main Authors: Peikari, Mohammad, Gangeh, Mehrdad J., Zubovits, Judit, Clarke, Gina, Martel, Anne L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-01-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bag of visual words; breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; fast k -means; Feature extraction; Female; Histograms; Humans; image analysis; Image color analysis; Image Interpretation, Computer-Assisted - methods; Machine Learning; Pathology; ROC Curve; texture; Training; Visualization
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2015.2470529

Purpose: Pathologists often look at whole slide images (WSIs) at low magnification to find potentially important regions and then zoom in to higher magnification to perform more sophisticated analysis of the tissue structures. Many automated methods of WSI analysis attempt to preprocess the down-sampled image in order to select salient regions which are then further analyzed by a more computationally intensive step at full magnification. Although it can greatly reduce processing times, this process may lead to small potentially important regions being overlooked at low magnification. We propose a texture analysis technique to ease the processing of H&E stained WSIs by triaging clinically important regions. Method: Image patches randomly selected from the whole tissue area were divided into smaller tiles and Gaussian-like texture filters were applied to them. Texture filter responses from each tile were combined together and statistical measures were derived from their histograms of responses. Bag of visual words pipeline was then employed to combine extracted features from tiles to form one histogram of words per every image patch. A support vector machine classifier was trained using the calculated histograms of words to be able to distinguish between clinically relevant and irrelevant patches. Result: Experimental analysis on 5151 image patches from 10 patient cases (65 tissue slides) indicated that our proposed texture technique out-performed two previously proposed colour and intensity based methods with an area under the ROC curve of 0.87. Conclusion: Texture features can be employed to triage clinically important areas within large WSIs.