CT texture analysis using the filtration-histogram method: what do the measurements mean?
By K. Miles, B. Ganeshan, M. P. Hayball. Cancer Imaging, 2013
296 citations recorded by the source corpus.
Abstract Analysis of texture within tumours on computed tomography (CT) is emerging as a potentially useful tool in assessing prognosis and treatment response for patients with cancer. This article illustrates the image and histological features that correlate with CT texture parameters obtained from tumours using the filtration-histogram approach, which comprises image filtration to highlight image features of a specified size followed by histogram analysis for quantification. Computer modelling can be used to generate texture parameters for a range of simple hypothetical images with specified image features. The model results are useful in explaining relationships between image features and texture parameters. The main image features that can be related to texture parameters are the number of objects highlighted by the filter, the brightness and/or contrast of highlighted objects relative to background attenuation, and the variability of brightness/contrast of highlighted objects. These relationships are also demonstrable by texture analysis of clinical CT images. The results of computer modelling may facilitate the interpretation of the reported associations between CT texture and histopathology in human tumours. The histogram parameters derived during the filtration-histogram method of CT texture analysis have specific relationships with a range of image features. Knowledge of these relationships can assist the understanding of results obtained from clinical CT texture analysis studies in oncology.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e6e216cf0ac675af64e4a8b4dbbc0af9e1fcec28
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review (2018, background): “Recent studies have suggested that complex shape metrics and the nonuniform appearance of the tumor mass in images (ie, texture) also provide information about the likely outcome of the disease (3, 10-12).”