Interpolated mean signatures and archetypal signatures for visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths (measured 4–14 dai).

<p>In the left column mean signatures of diseased barley plants before selecting disease archetypal signatures and in the right column mean archetypal signatures for <i>η</i> = 1 are illustrated. Archetypal signatures allow a better differentiation between different developing stages of the diseases. Moreover, they are in accordance to visually and manually extracted reflectance signatures during disease development. (Best viewed in color)</p

Single sketches of hyperspectral dynamics of plant diseases for visible-near infrared (VNIR) wavelengths.

<p>Each sketch consists of parts encoding major states during pathogenesis of the plant disease with similar weights. Thus, the shorter a part, the higher the impact of the corresponding period. (Best viewed in color)</p

Collective disease progression via Metro Maps of hyperspectral dynamics of diseased plants for visible-near infrared (VNIR) (top) and short-wave infrared (SWIR) wavelengths (bottom).

<p>Each disease track from hyperspectral images exhibits a specific route in the metro map, the direction and the dynamic steps are in correspondence to biophysical and biochemical processes during disease development. The beginning of all routes is at the same time point/train station (day of inoculation, gray circle). (Best viewed in color)</p

Disease archetypal selections.

<p>An example image showing diseased barley plants (RGB, first column) with powdery mildew (first row), net blotch (second row) and rust (third row) 14 dai. False color images present automatically determined diseased plant pixels based on disease archetypal signatures for VNIR and SWIR data (middle and right columns). The yellower/redder the color, the greather the difference of the pixel to a healthy plant. (Best viewed in color)</p

Characteristic phenotypes of healthy and diseased barley leaves.

<p>(<b>A</b>) Healthy barley leaf, (<b>B</b>) net blotch caused by <i>Pyrenophora teres</i>, (<b>C</b>) brown rust caused by <i>Puccinia hordei</i>, and (<b>C</b>) powdery mildew caused by <i>Blumeria graminis hordei</i>.</p

Automated and efficient interpretation of 3D wound models by non-invasive hyperspectral imaging <i>in vitro</i>.

<p>Experimental design and work flow showing the different steps for monitoring wound healing from hyperspectral imaging data to interpretation using an efficient approach for unsupervised classification of wound tissue based on hierarchical decomposition according to archetypal data points.</p

Comparison for Kmeans and XHC for different number of clusters.

<p>With increasing number of clusters, Kmeans resulted in more clusters around the wound as this area represented the major part of the tissue. XHC further highlighted the middle part of the images, which was the result of a hierarchical decomposition of the signatures. This allowed a better investigation and comparison of the wound tissue and healing progress.</p

Representative spectra of 3-dimensional wound models.

<p>(a) Cluster representatives after computing of XHC for the hyperspectral image data for. The number of cluster was set to <i>k</i> = 7 that could reflect biological processes in this cell culture system. The corresponding signatures represented different regions of the tissues. The dotted line was produced by a part of the tissue covered with fluid resulting in overexposure during the measurement. (b-c) The quantification of pixel densities per cluster. The y-axis is shown in log scale. AWF and CWF induced no significant differences in pixel densities compared to the control situation, however, the dark blue cluster was absent after 10 days <i>in vitro</i> without any supplements.</p

Histological classification of hyperspectral cluster means in relation to wound healing processes.

<p>(a) H/E staining was performed to monitor the wound healing progress morphologically over a specific time period <i>in vitro</i>. The different stages were presented as false color zoomed images of the hyperspectral clusters for the wounds. (b) The quantification of the cell number in different regions of interests revealed the first time a correlation between spectral reflectance and cell quantity in the tissue. (c) Immunohistochemical investigation of CXCR4, a marker for migratory cells, determined no correlations to reflectance data. Additionally, the cells generating the characteristic hyperspectral signature did not correspond to Caspase 3-expressing apoptotic cells (not shown). Scale bar: 250 <i>μ</i>m.</p