The use of modern medical equipment in crisis and war zones for emergency medical teams (EMT) of the World Health Organization is an important factor for fast and efficient humanitarian aid. A reliable vital parameter monitoring is fundamental in mobile hospitals. Currently, the maintenance of medical devices in structurally weak areas is difficult due to the company’s proprietary standards. Rough environmental influences such as dust, moisture, heat or shocks can lead to dysfunktion and long-lasting failure of instrumentation. Pulse oximetry and blood pressure measurements are particularly susceptible. We developed an open source vital parameter monitoring system for use under adverse conditions and structurally weak areas. Blood oxygen levels, heart rate, blood pressure and electrocardiograms are recorded and transferred to decentralized displays. The main focus is on reliability and robustness of various optical sensors for pulse oximetry, the repair capability of the system also for non-technical personnel and the availability of individual standard components. Therefore we implemented a monitoring system basing on individual microcontrollers for each vital parameter. Different optical sensors for measurement in transmission and reflection were tested at suitable body sites with near-surface arteries. In combination with the electrocardiogram, evaluation of the pulse transit time enables continuous blood pressure measurements. A specially developed optical reflective sensor allows reliable measurement of blood oxygen level. For extended blood pressure measurements, the pulsetransit-time method (PTT) was implemented and enables a continuous monitoring. Even in emergencies, the trend in blood pressure can be monitored with PTT without prior calibration. The reliability was investigated.
Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the
gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound
imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis
and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS
image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and
tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based
segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent
feature model is built by supervised learning using a set of manually segmented needles. The feature space is
spanned by common binary object features as size and eccentricity as well as imaging-system dependent features
like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization
of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker
lines are removed from the images. The segmentation itself is realized by scale-invariant classification using
maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could
be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for
biopsy needle localization in clinical prostate biopsy TRUS images.