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Glucometers present an important self-monitoring tool for diabetes patientsand therefore must exhibit high accu- racy as well as good usability features.Based on an invasive, photometric measurement principle that drasticallyreduces the volume of the blood sample needed from the patient, we present aframework that is capable of dealing with small blood samples, whilemaintaining the required accuracy. The framework consists of two major parts:1) image segmentation; and 2) convergence detection. Step 1) is based oniterative mode-seeking methods to estimate the intensity value of the region ofinterest. We present several variations of these methods and give theoreticalproofs of their convergence. Our approach is able to deal with changes in thenumber and position of clusters without any prior knowledge. Furthermore, wepropose a method based on sparse approximation to decrease the computationalload, while maintaining accuracy. Step 2) is achieved by employing temporaltracking and prediction, herewith decreasing the measurement time, and, thus,improving usability. Our framework is validated on several real data sets withdifferent characteristics. We show that we are able to estimate the underlyingglucose concentration from much smaller blood samples than is currentlystate-of-the- art with sufficient accuracy according to the most recent ISOstandards and reduce measurement time significantly compared tostate-of-the-art methods.

Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes