Example result given by the segmentation algorithm when a 40× image is input.

<p>Green contours identify microvessels exhibiting significant stain concentration.</p

Real parts of the Gabor filter bank.

<p>Generated for different combinations of <i>θ</i> (in radians) and <i>f</i> (in Hz) with , and <i>ϕ</i> = 0.</p

Overview of the workflow.

<p>Input immunohistochemistry (IHC) images are pre-segmented to identify candidate structures of interest, which are represented within a generated mask image. Candidate structures within the mask image are filtered using a decision tree derived from training sessions to produce a fully segmented IHC image. For further details, see text.</p

False negative classification rate.

<p>FNR of microvessel classification for synthetic data as a function of image entropy. Average FNR = 7.49.</p

Example result given by the segmentation algorithm.

<p>Shown is an IHC image plane within the bregma stained for GLUT1 expression in vascular structures. Green contours identify microvessels exhibiting significant GLUT1 concentration.</p

GLUT1 stained image examples.

<p>(top) Synthetic and (bottom) non-synthetic images with varying global pixel entropy (<i>H</i>) Local spatial frequency tends to increase with local entropy. IHC images with higher <i>H</i> usually exhibit more spatially complex surface geometries and/or possess increased surface noise due to the staining protocol.</p

Execution time profile for the automated protein localization algorithm.

<p>Execution time profile for the automated protein localization algorithm.</p

Spike-and-slab analysis, dark period.

<p> = number of episodes, μ = average duration of slabs (long bouts, in epochs), π = probability that the animal is in a spike (short bout). Mean ± standard deviation. Spike-and-slab analysis of each state during the dark period for young and old mice. n</p><p> p<0.00625 (Bonferroni correction).</p

Pixel-wise classification of the hippocampal region using k-means clustering.

<p>White pixels mark regions potentially containing vascular structures of interest. Black pixels mark non-vascular structures.</p

Image Entropy Range and False Positive Rate of microvessel classification.

<p>(top) Boxplot showing the median, standard deviation, and range of entropy values for both synthetic and non-synthetic datasets. (bottom) False positive rate (FPR) of microvessel classification for synthetic and non-synthetic IHC images as a function of image entropy.</p