A set of monolithic stationary phases representing a broad span of monomers and porogens have been characterized directly in their capillary chromatographic format by computational assessment of their pore structure from transmission electron micrographs obtained after in situ embedment of the monoliths in contrast resin, followed by dissolution of the fused-silica tubing, further encasement of the resin-embedded monolith, and microtomy. This technique has been compared to mercury intrusion, a more conventional technique for macroporosity estimation. Supplementing the embedding resin by lead methacrylate gave a negative staining, and the resulting micrographs showed a good contrast between the polymeric monoliths and the embedding resin that allowed studies on the pore formation and polymer development. The technique was also applied to a commercial monolithic silica column.

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of pancreatic tissue, such as the rat pancreas, and to increase the number of channels (cell types) that may be studied in a single specimen. We further describe the implementation of a number of computational tools that provide: 1/ accurate positioning of a specimen's (in our case the pancreas) centre of mass (COM) at the axis of rotation (AR) 2 ; 2/ improved algorithms for post-alignment tuning which prevents geometric distortions during the tomographic reconstruction 2 and 3/ a protocol for intensity equalization to increase signal to noise ratios in OPT-based BCM determinations 3. In addition, we describe a sample holder that minimizes the risk for unintentional movements of the specimen during image acquisition. Together, these protocols enable assessments of BCM distribution and other features, to be performed throughout the volume of intact pancreata or other organs (e.g. in studies of islet transplantation), with a resolution down to the level of individual islets of Langerhans.

Since it was first presented in 2002, optical projection tomography (OPT) has emerged as a powerful tool for the study of biomedical specimen on the mm to cm scale. In this paper, we present computational tools to further improve OPT image acquisition and tomographic reconstruction. More specifically, these methods provide: semi-automatic and precise positioning of a sample at the axis of rotation and a fast and robust algorithm for determination of postalignment values throughout the specimen as compared to existing methods. These tools are easily integrated for use with current commercial OPT scanners and should also be possible to implement in "home made" or experimental setups for OPT imaging. They generally contribute to increase acquisition speed and quality of OPT data and thereby significantly simplify and improve a number of three-dimensional and quantitative OPT based assessments.

Abstract-This paper describes a classification based tree detection method for autonomous navigation of forest vehicles in forest environment. Fusion of color, and texture cues has been used to segment the image into tree trunk and background objects. The segmentation of images into tree trunk and background objects is a challenging task due to high variations of illumination, effect of different color shades, non-homogeneous bark texture, shadows and foreshortening. To accomplish this, the approach has been to find the best combinations of color, and texture descriptors, and classification techniques. An additional task has been to estimate the distance between forest vehicle and the base of segmented trees using monocular vision. A simple heuristic distance measurement method is proposed that is based on pixel height and a reference width. The performance of various color and texture operators, and accuracy of classifiers has been evaluated using cross validation techniques.

We demonstrate a technique to improve structural data obtained from Optical Projection Tomography (OPT) using Image Fusion (IF) and contrast normalization. This enables the visualization of molecular expression patterns in biological specimens with highly variable contrast values. In the approach, termed IF-OPT, different exposures are fused by assigning weighted contrasts to each. When applied to projection images from mouse organs and digital phantoms our results demonstrate the capability of IF-OPT to reveal high and low signal intensity details in challenging specimens. We further provide measurements to highlight the benefits of the new algorithm in comparison to other similar methods.

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Computer support for early detection of breast cancer requires a proper mimicking of the way radiologists compare mammographic images; by comparing bilateral (images of the left and right breasts) and temporal images. In this paper, one method for bilateral registration and intensity normalization and two methods for difference analysis are described. The bilateral registration is based on anatomical features and assumptions of how the female breast is deformed under compression. The first method for differential analysis is based on the absolute difference between the registered images while the second method is based on statistical differences between properties of corresponding neighborhoods. The methods are tested on images from the MIAS database (on 100 images with 59 abnormalities distributed over four types) and evaluated by FROC-analysis. The performances of the two methods are similar but the statistical method gives better performance at a lower false positive rate and is better in particular for detecting asymmetrical developments.

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