What are the types of features extracted in radiomics?
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Radiomics involves extracting various types of features from medical images that are not readily visible to the human eye. These features include first-order statistics, shape characteristics, gray-level characteristics, and deep radiomic features, which are abstract features derived from deeper layers of convolutional neural networks . Additionally, radiomics can encompass texture and morphological features, as well as features extracted from pretrained deep networks, providing a wide range of information for diagnosis, prognosis, and therapy planning . The extraction of these diverse features allows for a comprehensive analysis of medical images, aiding in the identification of patterns and characteristics that can be valuable in clinical decision-making processes.
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10 Apr 2023 | Radiomics extracts first-order statistics, 2D/3D shape, and gray level features from medical images for tumor characterization, aiding in diagnosis, prognosis, and therapy in cancer research. |
1 Citations | Radiomics extracts features like SUV, intensity histograms, and deep radiomic features from radiological data, enhancing imaging insights beyond basic quantitation for semantic reporting in veterinary radiology. |
| Radiomics typically extracts generic texture and morphological features, and alternatively, features extracted from pretrained deep networks are used, impacting predictive performance in radiomic models. | |
| Radiomics extracts CT texture features like those studied in MATLAB and Pyradiomics implementations, focusing on reproducibility in lung and head/neck cancer datasets. |
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