Image enhancement variational methods for enabling strong cost reduction in OLED-based point-of-care immunofluorescent diagnostic systems.

Immunofluorescence diagnostic systems cost is often dominated by high-sensitivity, low-noise CCD-based cameras that are used to acquire the fluorescence images. In this paper, we investigate the use of low-cost CMOS sensors in a point-of-care immunofluorescence diagnostic application for the detection and discrimination of 4 different serotypes of the Dengue virus in a set of human samples. A 2-phase postprocessing software pipeline is proposed, which consists in a first image-enhancement stage for resolution increasing and segmentation and a second diagnosis stage for the computation of the output concentrations. We present a novel variational coupled model for the joint super-resolution and segmentation stage and an automatic innovative image analysis for the diagnosis purpose. A specially designed forward backward-based numerical algorithm is introduced, and its convergence is proved under mild conditions. We present results on a cheap prototype CMOS camera compared with the results of a more expensive CCD device, for the detection of the Dengue virus with a low-cost OLED light source. The combination of the CMOS sensor and the developed postprocessing software allows to correctly identify the different Dengue serotype using an automatized procedure. The results demonstrate that our diagnostic imaging system enables camera cost reduction up to 99%, at an acceptable diagnostic accuracy, with respect to the reference CCD-based camera system. The correct detection and identification of the Dengue serotypes have been confirmed by standard diagnostic methods (RT-PCR and ELISA).