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Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach
- Source :
- IEEE transactions on medical imaging. 35(1)
- Publication Year :
- 2015
-
Abstract
- 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.
- Subjects :
- Computer science
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Breast Neoplasms
02 engineering and technology
Texture (music)
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
Breast cancer
Image texture
Histogram
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Computer vision
Electrical and Electronic Engineering
Zoom
ComputingMethodologies_COMPUTERGRAPHICS
Radiological and Ultrasound Technology
business.industry
Pattern recognition
Filter (signal processing)
medicine.disease
Computer Science Applications
ROC Curve
Bag-of-words model in computer vision
020201 artificial intelligence & image processing
Female
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 35
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....f8d1659b382ee21867c632c82ad7fe8c