An algorithm for fast adaptive image binarization with applications in radiotherapy imaging.

Locally adaptive image binarization with a sliding-window threshold can be an effective tool for various image processing tasks. We have used the method for the detection of bone ridges in radiotherapy portal images. However, a straight-forward implementation of sliding-window processing is too time consuming for routine use. Therefore, we have developed a new thresholding criterion suitable for incremental update within the sliding window, and we show that our algorithm gives better results on difficult portal images than various publicly available adaptive thresholding routines. For small windows, the routine is also faster than an adaptive implementation of the Otsu algorithm that uses interpolation between fixed tiles, and the resulting images are equally good.

Standardization work on personalized eHealth systems.

For widespread adoption of eHealth, and to achieve eInclusion and eAccessibility, eHealth systems must be tailored to each individual user's needs and preferences. Many eHealth products and services contain adjustable parameter settings, but they are specific to each product and unrelated to each other. This paper describes ongoing work to establish standards and guidelines for personalization of eHealth systems, taking into account the needs of all users, both clients and caregivers. The standard builds on a generic 'user profile', which stores data about the users, their preferences and their context. This profile can then be used by eHealth services and devices to ensure a user experience tailored to each person. The work surveys relevant areas of personalization, like identity management, profile management etc, addressing in depth those aspects of personalization that are specific to eHealth: User capabilities, care provider roles and functions, health related information, and confidentiality measures.

Wireless transfer of sensor data into electronic health records.

The purpose of this study is to explore how wireless transfer of sensor data can be implemented in existing Electronic Health Record (EHR) systems. Blood glucose data from people with diabetes Type 1 has been selected as the case.As proof of concept, a prototype for sending blood glucose measurements into an EHR system was developed for the DIPS EHR system. For the prototype to be transferable to a general setting, care was taken not to introduce any additional workload for the diabetes nurses or the diabetes Type 1 patients. In the prototype, the transfer of blood glucose data is automatic and invisible to the user, and the data is presented to the nurses within the existing DIPS laboratory module.To determine whether deployment of such a system would present any risks or hazards to patients (medical or financial), a risk analysis was performed. The analysis indicates that storing blood glucose values in the patient's EHR does not represent any significantly increased risks for the diabetes patient.The study shows that existing EHR systems are well suited to receive sensor data. The three main EHR systems in Norwegian hospitals are all supported with application programming interfaces (APIs), enabling external vendors to add modules. These APIs are sufficient to implement modules for receiving sensor data. However, none of the systems currently have commercially available modules for receiving such data.