Combination of Digital Image Processing and Statistical Data Segmentation to Enhance SPR and SPRi Sensor Responses.

The work reports the combination of basic digital image processing (DIP) techniques and statistical segmentation strategy (SDS) to improve surface plasmon resonance curve (SPRc) and SPR imaging (SPRi) sensors' performance. The SPR image is used for sensing and monitoring biological events in the so-called SPR imaging process. In the traditional SPR process based on the attenuated total reflection (ATR) method, the image is used to create the SPR curve, and the curve features tracking is employed on sensing applications. The SPR curve features are enhanced after the pixels of the SPR image have been processed with low-complexity filters in the spatial domain (brightness, contrast, threshold, and morphological). The bootstrap was used as a statistical processing approach, selecting lines and columns from the image that was less affected by imperfections and noises in the image detector, and consequently reducing the SPR sensor instrumentation disturbances. Experimental tests with reversible binding water-mixture were performed, and both image and statistical processing were reported. The combination of DIP and SDS approaches improves the extraction of the curve features, increasing the performance in terms of resonance position sensitivity to 81%. [ABSTRACT FROM AUTHOR]