Skip to main content
. Author manuscript; available in PMC: 2018 Jun 11.
Published in final edited form as: Med Image Comput Comput Assist Interv. 2017 Sep 4;10435:81–88. doi: 10.1007/978-3-319-66179-7_10

Fig. 2.

Fig. 2

Representation of the unique iterative classification method: (a) multi-parameter MRI input images, (b) multi-class classifier, in our case a random forest, (c) tissue-specific probability maps output by the classifier, (d) structured and rotationally-invariant label context representations are computed and used as input to another classifier, in addition to the original multi-parameter MRI imaging, (f) tissue-specific probability maps output by the classifier, and the entire process is iterated.