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At the time the article was created Candace Makeda Moore had no recorded disclosures.View Candace Makeda Moore's current disclosures
At the time the article was last revised Bahman Rasuli had no recorded disclosures.View Bahman Rasuli's current disclosures
Feature extraction is a process utilized in both machine learning and image processing by which data is transformed into a smaller more relevant set of data. Feature extraction is a type of dimensionality reduction. Feature extraction can be performed on texts as part of NLP or on images for computer vision tasks. In terms of radiological imaging, in addition to the raw data of an image, many other numbers may represent aspects of the image. Feature extraction is a mathematical way of abstracting from an image the relevant components. Some specific examples of types of algorithms often used in feature extraction are principle component analysis and linear discriminant analysis. Feature extraction is fundamental to many machine learning algorithms.
- 1. Laszlo Papp, Ivo Rausch, Marko Grahovac, Marcus Hacker, Thomas Beyer. Optimized Feature Extraction for Radiomics Analysis of 18F-FDG PET Imaging. (2019) Journal of Nuclear Medicine. 60 (6): 864. doi:10.2967/jnumed.118.217612 - Pubmed
- 2. Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak. Radiomics: Images Are More than Pictures, They Are Data. (2015) Radiology. 278 (2): 563-77. doi:10.1148/radiol.2015151169 - Pubmed