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. 2020 Jul 31;2(4):e190047. doi: 10.1148/rycan.2020190047

Figure 1:

Texture or radiomic analysis for extraction of quantitative features reflecting higher-order pixel positions and relationships. Simplified diagram demonstrates two squares containing an identical number of white, gray, and black pixels. Using basic first-order statistical quantitative parameters, such as average or standard deviation, frequently used in traditional region of interest analysis, the boxes would have identical values, even though on visual inspection the patterns are clearly different. The objective of computerized image analysis approaches like texture or radiomic analysis is to extract quantitative parameters or features that capture the more complex, higher-order characteristics, such as those reflecting pixel positions and relationships.

Texture or radiomic analysis for extraction of quantitative features reflecting higher-order pixel positions and relationships. Simplified diagram demonstrates two squares containing an identical number of white, gray, and black pixels. Using basic first-order statistical quantitative parameters, such as average or standard deviation, frequently used in traditional region of interest analysis, the boxes would have identical values, even though on visual inspection the patterns are clearly different. The objective of computerized image analysis approaches like texture or radiomic analysis is to extract quantitative parameters or features that capture the more complex, higher-order characteristics, such as those reflecting pixel positions and relationships.