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Although tissue microarrays (TMA) have been widely used for a number of years, it is still not clear how many core biopsies should be taken to determine a reliable value for percentage positivity or how much heterogeneity in marker expression influences this number. The first aim of this study was to validate the human visual semi-quantitative scoring system for positive staining of tumour tissue with the exact values determined from computer-generated images. The second aim was to determine the minimum number of core biopsies needed to estimate percentage positivity reliably when the immunohistochemical staining pattern is heterogeneous and scored in a non-binary way. Tissue sections from ten colorectal cancer specimens were stained for carbonic anhydrase IX (CA IX). The staining patterns were digitized and 400 artificial computer-generated images were generated to test the accuracy of the human scoring system. To determine the minimal number of core biopsies needed to account for tumour heterogeneity, 50 (artificial) core biopsies per section were taken from the tumoural region of the ten digitally recorded full tissue sections. Based on the semi-quantitative scores from the 50 core biopsies per section, 2500 x n (n = 1-10 core biopsies) experimental core biopsies were then generated and scores recorded. After comparison with field-by-field analysis from the tumoural region of the whole tissue section, the number of core biopsies that need to be taken to minimize the influence of heterogeneity could be determined. In conclusion, visual scoring accurately estimated the percentage positivity and the percentage tumour present in a section, as judged by comparison with the artificial images. The exact number of core biopsies that has to be examined to determine tumour marker positivity using TMAs is affected by the degree of heterogeneity in the expression pattern of the protein, but for most purposes at least four is recommended.

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Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many anomaly detectors have been proposed in literature. They differ by the way the background is characterized and by the method used for determining the difference between the current pixel and the background. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the background of the complete scene by a single multi-variate normal distribution. In many cases this model is not appropriate for describing the background. For that reason a variety of other anomaly detection methods have been developed. This paper examines three classes of anomaly detectors: sub-space methods, local methods and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with different backgrounds. The results are evaluated and compared.