Tests that depend on changes in color are commonly used in biosensing. Here, we report on a colorimetric reader for such applications. The device is simple to construct and operate, making it ideal for research laboratories with limited resources or skilled personnel. It consists of a commercial multispectral sensor interfaced with a Raspberry Pi and a touchscreen. Unlike camera-based readers, this instrument requires no calibration of wavelengths by the user or extensive image processing to obtain results. We demonstrate its potential for colorimetric biosensing by applying it to the birefringent enzyme-linked immunosorbent assay. It was able to prevent certain false positives that the assay is susceptible to and lowered its limit of detection for glucose by an order of magnitude.

The primary goal of this research was to provide image processing support to aid in the identification of those subjects most affected by bone loss when exposed to weightlessness and provide insight into the causes for large variability. Past research has demonstrated that genetically distinct strains of mice exhibit different degrees of bone loss when subjected to simulated weightlessness. Bone loss is quantified by in vivo computed tomography (CT) imaging. The first step in evaluating bone density is to segment gray scale images into separate regions of bone and background. Two of the most common methods for implementing image segmentation are thresholding and edge detection. Thresholding is generally considered the simplest segmentation process which can be obtained by having a user visually select a threshold using a sliding scale. This is a highly subjective process with great potential for variation from one observer to another. One way to reduce inter-observer variability is to have several users independently set the threshold and average their results but this is a very time consuming process. A better approach is to apply an objective adaptive technique such as the Riddler / Calvard method. In our study we have concluded that thresholding was better than edge detection and pre-processing these images with an iterative deconvolution algorithm prior to adaptive thresholding yields superior visualization when compared with images that have not been pre-processed or images that have been pre-processed with a filter.