Aims: To develop and implement an automated virtual slide screening system that distinguishes normal histological findings and several tissue -based crude (texture -based) diagnoses. Theoretical considerations:Virtual slide technology has to handle and transfer images of GB Bytes in size. The performance of tissue based diagnosis can be separated into a) a sampling procedure to allocate the slide area containing the most significant diagnostic information, and b) the evaluation of the diagnosis obtained from the information present in the selected area. Nyquist's theorem that is broadly applied in acoustics, can also serve for quality assurance in image information analysis, especially to preset the accuracy of sampling. Texture -based diagnosis can be performed with recursive formulas that do not require a detailed segmentation procedure. The obtained results will then be transferred into a "self-learning" discrimination system that adjusts itself to changes of image parameters such as brightness, shading, or contrast.Methods: Non-overlapping compartments of the original virtual slide (image) will be chosen at random and according to Nyquist's theorem (predefined error-rate). The compartments will be standardized by local filter operations, and are subject for texture analysis. The texture analysis is performed on the basis of a recursive formula that computes the median gray value and the local noise distribution. The computations will be performed at different magnifications that are adjusted to the most frequently used objectives (*2, *4.5, *10, *20, *40). The obtained data are statistically analyzed in a hierarchical sequence, and in relation to the clinical significance of the diagnosis. Results:The system has been tested with a total of 896 lung cancer cases that include the diagnoses groups: cohort (1) normal lung -cancer; cancer subdivided: cohort (2) small cell lung cancer -non small cell lung cancer; non small cell lung cancer subdivided: cohort (3) squamous cell carcinoma -adenocarcinoma -large cell carcinoma. The system can classify all diagnoses of the cohorts (1) and (2) correctly in 100%, those of cohort (3) in more than 95%. The percentage of the selected area can be limited to only 10% of the original image without any increased error rate. Conclusion:The developed system is a fast and reliable procedure to fulfill all requirements for an automated "prescreening" of virtual slides in lung pathology.

In this paper we present the first high resolution SAR measurements performed in the lower terahertz region with the MIRANDA-300 system. The millimeter wave FMCW radar is operated at 300 GHz and provides a bandwidth of more than 40 GHz leading to a range resolution of a few millimeters. With an output power of around 5 mW over the complete bandwidth the system is mainly designed for short range applications up to several hundreds of meters. The capability of the radar has also been investigated in ISAR measurements. The presented results demonstrate the high image quality and the richness of details

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Simulation-based training is becoming an accepted tool for educating physicians before direct patient care. As ultrasound-guided regional anesthesia (UGRA) becomes a popular method for performing regional blocks, there is a need for learning the technical skills associated with the technique. Although simulator models do exist for learning UGRA, they either contain food and are therefore perishable or are not anatomically based. We developed 3 sonoanatomically based partial-task simulators for learning UGRA: an upper body torso for learning UGRA interscalene and infraclavicular nerve blocks, a femoral manikin for learning UGRA femoral nerve blocks, and a leg model for learning UGRA sciatic nerve blocks in the subgluteal and popliteal areas.