Multivariate Image Processing Technique for Noninvasive Glucose Sensing

摘要

A potential noninvasive glucose sensing technique was investigated for application towards in vivo glucose monitoring for individuals afflicted with diabetes mellitus. Three dimensional ray tracing simulations using a realistic iris pattern integrated into an advanced human eye model are reported for physiological glucose concentrations ranging between 0 to 500 mg/dL. The anterior chamber of the human eye contains a clear fluid known as the aqueous humor. The optical refractive index of the aqueous humor varies on the order of 1.5×10~(-4) for a change in glucose concentration of 100 mg/dL. The simulation data was analyzed with a developed multivariate chemometrics procedure that utilizes iris-based images to form a calibration model. Results from these simulations show considerable potential for use of the developed method in the prediction of glucose. For further demonstration, an in vitro eye model was developed to validate the computer based modeling technique. In these experiments, a realistic iris pattern was placed in an analog eye model in which the glucose concentration within the fluid representing the aqueous humor was varied. A series of high resolution digital images were acquired using an optical imaging system. These images were then used to form an in vitro calibration model utilizing the same multivariate chemometric technique demonstrated in the 3-D optical simulations. In general, the developed method exhibits considerable applicability towards its use as an in vivo platform for the noninvasive monitoring of physiological glucose concentration.