lichen planus mucosae. RESULTS: The stepwise application of 2 additional approaches (morphology, DNA content, argyrophilic nucleolar organizer region counts) increased the specificity of conventional cytologic diagnosis from 92.6% to 100%. This feasibility study provided a proof of concept, demonstrating efficiency,

Capturing natural scenes with high dynamic range content using conventional RGB cameras generally results in saturated and underexposed and therefore compromising image areas. Furthermore the image lacks color accuracy due to a systematic color error of the RGB color filters. The problem of the limited dynamic range of the camera has been addressed by high dynamic range imaging 1, 2 (HDRI): Several RGB images of different exposures are combined into one image with greater dynamic range. Color accuracy on the other hand can be greatly improved using multispectral cameras, 3 which more accurately sample the electromagnetic spectrum. We present a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.

Silver staining of cytopathologic specimens offers advantages in cancer diagnostics. A difficulty with such stained cell specimens is the very high dynamic range needed by the imaging system to appropriately cover the varying stain intensities. Beside those images of cell nuclei that can be used for the diagnostic interpretation, there are nuclei that appear too dark to observe their relevant properties, the so-called argyrophilic nucleolar organizer regions (AgNORs), which appear as spot-like areas darker than their immediate surroundings. We therefore show how high dynamic range images of nuclei can help to correctly segment the AgNORs. To this end, we acquire a sequence of differently exposed images, which are then combined into a high dynamic range image. Based on the dynamic range of the image signal within the segmented cell area, we compute another image which provides optimal contrast over this area of interest. To further increase the contrast for dark objects, a suitable nonlinear point transform is simultaneously applied. We provide examples of the thus generated images and their corresponding segmentations.

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