To reduce computational complexity, 2-D images of tumors are firstly preprocessed following our method to form two 1-D sequences, called horizontal and vertical landscapes, and then their fractal dimension is estimated using Higuchi's fractal dimension.
The fractal dimension is estimated by the slope of the regression line between the size of the boxes (1, 2, 4, 8, and 16 pixels) and the number of the boxes needed to encompass the binary object.
The affective content of w, for each dimension, is estimated as a linear combination of its semantic similarities to a set of K seed words and the corresponding affective ratings of seeds (for the corresponding dimension), for more details see [92].
Using this principle, the sublingual glycocalyx dimension was estimated using SDF imaging [ 17].
The fractal dimension was estimated using the box counting technique of leaf scans.
The glycocalyx dimension was estimated in two different vascular beds using sidestream dark field imaging and combined fluorescein/indocyanine green angiography for sublingual and retinal vessels, respectively.
For vessels with diameters ranging from 10 to 20 μm, a functional estimate of glycocalyx dimension was estimated by comparing the 50th percentile of erythrocyte width with the 90th percentile of erythrocyte width (Fig. 1).
The fractal dimension was estimated by Box-counting method, using the software ImageJ (National Institute of Health, Bethesda, USA), widely used in the literature and available free on the Internet (http://rsbweb.nih.gov/ij/ ).
In the latter analysis, the latent variable dimension was estimated to be 37 by the RMT method of Teschendorff et al. (2011); because simulations suggest accurate dimension estimation by the RMT method, we used d = 37. 500 bootstrap samples were used for inference.
