Experimental Evaluation and Analysis of LED Illumination Source for Endoscopy Imaging

The minimally invasive surgery uses a small instrument with camera and light to fit the tiny cut in the skin. The selection of the light depends on the power and driving current of the circuit. It can also help in the standardization of the camera and capture the tissues' true-colour image. This paper presents the LED source analysis used in the clinical endoscopes for surgery and the human body's medical examination. Initially, a LED source selection mechanism generating intense illuminance in a visible band is proposed. A low-cost prototype model is developed to analyze the wavelength and illuminance of three different LEDs types. An effect on variation in LED illumination is investigated by changing the distance between the Borescope and LED source. True-colour image generation and tissue contrast are more important in medical diagnostics. Therefore, a sigmoid function improving the whole contrast ratio of the captured image in real-time is presented. The results of spectrum and wavelength for a current variation are presented. Type 3 LED produces higher illumination (i.e., 395 Klux) and peak wavelength (i.e., 622.05 nm) than other LEDs, while type-2 LED has better FWHM for the blue colour spectrum. The modification in the sigmoid function enhances the image with 34.25 peak PSNR producing a true-colour image

Development and On-sky Demonstration of Atmospheric Turbulence Profiler for Future Adaptive Optics with Multiple Laser Guide Stars

Tohoku University博士（理学）thesi

Novel image enhancement technique using shunting inhibitory cellular neural networks

This paper describes a method for improving image quality in a color CMOS image sensor. The technique simultaneously acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, identify local features, achieve color constancy, and lightness rendition. An efficient hardware architecture and a rigorous analysis of the different modules are presented to achieve high quality CMOS digital camera

Digital Image Processing And Metabolic Parameter Linearity To Noninvasively Detect Analyte Concentration

Spectroscopy is the scientific technique of quantifying and measuring electromagnetic, or light, reflectance or absorption. Atoms emit and/or absorb light when light passes through. The excitations provide specific energy signatures that relate to the element that is emitting or absorbing the light. Non-invasive biosensors monitor physical health properties such as heart rate, oxygen saturation, and tissue blood flow as a result of spectroscopy. Several attempts have been made to non-invasively detect metabolic chemical, or analyte, concentration with various spectroscopic techniques. The primary limitation is due to signal-to-noise ratio. This research focuses on a unique method that combines emission spectroscopy and machine learning to non-invasively detect glucose and other metabolic analyte concentrations. Artificial neural network is applied to train a predictive model that enables the remote sensing capability. The data acquisition requires capturing digital images of the spectral reflectance. Image processing and segmentation determines discrete variables that correlate with the metabolic analytes. The clinical trial protocol includes n=90 subjects, and a venipuncture comprehensive metabolic panel blood test within two minutes prior to a non-invasive spectral reading. Results indicate a strong correlation between the spectral system and the clinical gold standard, relative to metabolic analyte concentration