2D amorphous silicon arrays can be sued for medical imaging, non-destructive testing, and high-speed document scanning. We have built a 200 spi imaging system with an active area containing 2304 X 3200 pixels, the largest amorphous silicon imaging system described to data. Packaged with the array are peripheral electronics which include active matrix drivers, charge sensitive amplifiers, two 12 bit A/D converters, and control logic. Digital data travel via fiber to a frame grabber in a personal computer. Software includes gain/offset corrections, line and pixel corrections, window and level controls, and a user interface. Through a combination of layout optimization, amplifier design, and system timing, we have demonstrated a noise level of 1.5 Ke RMS and a signal to noise ratio of 1900.
Proc. SPIE. 3036, Medical Imaging 1997: Image Perception
KEYWORDS: Visual process modeling, Tumors, Imaging systems, Sensors, Interference (communication), Medical imaging, Image quality, Human vision and color perception, Performance modeling, Systems modeling
We demonstrate that human-vision-model-based image quality metrics not only correlate strongly with subjective evaluations of image quality but also with human observer performance on visual recognition tasks. By varying amorphous silicon image system design parameters, the performance of human observers in target identification using the resulting test images was measured, and compared with the target weighted just-noticeable-difference produced by a human vision model applied to the same set of images. The detectability of model observer with the human observer was highly correlated for a wide range of image system design parameters. These results demonstrate that the human vision model can be used to produce human observer performance optimized imaging systems without the need for extensive human trials. The human vision based tumor detectors represent a generalization of channelized Hotelling models to non-linear, perceptually based models.
Because of the complex response of the human visual system, typical measurements of image system quality such as the detective quantum efficiency, mean transfer function, and signal-to- noise ratio cannot always be used to determine conditions for optimal perceptual image quality. Using a model of the human vision system, the ViDEOS/Sarnoff Human Vision Discrimination Model (HVM), this work demonstrates that human vision models provide a promising quantitative measure of image perceptual quality. The model requires an image and a matching reference image in order to determine the perceptual difference between the images at each point. A simple model of a digital amorphous silicon medical x-ray system is used to create the necessary images as a function of various design parameters. The image pairs are then analyzed by the HVM. In all cases the dependence of perceived image quality closely follows measures of image quality as determined by the HVM for many image system design variations. Increasing the detector size actually increases the image quality in the presence of either readout or input noise. The model was also used to optimize the image system for a specific task optimization. As an example, the effect of system design parameters on tumor identification in mammographic images is determined.
We designed, fabricated, and tested an optically addressed spatial light modulator (SLM) that performs a 3 X 3 kernel image convolution using ferroelectric liquid crystal on VLSI technology. The chip contains a 16 X 16 array of current-mirror-based convolvers with a fixed kernel for finding edges. The pixels are located on 75 micron centers, and the modulators are 20 microns on a side. The array successfully enhanced edges in illumination patterns. We developed a high-level simulation tool (CON) for analyzing the performance of convolving SLM designs. CON has a graphical interface and simulates SLM functions using SPICE-like device models. The user specifies the pixel function along with the device parameters and nonuniformities. We discovered through analysis, simulation and experiment that the operation of current-mirror-based convolver pixels is degraded at low light levels by the variation of transistor threshold voltages inherent to CMOS chips. To function acceptable, the test SLM required the input image to have an minimum irradiance of 10 (mu) W/cm2. The minimum required irradiance can be further reduced by adding a photodarlington near the photodetector or by increasing the size of the transistors used to calculate the convolution.
An optical processor for zero-crossing edge detection is presented which consists of two defocused imaging systems to perform the Gaussian convolutions and a VLSI, ferroelectric liquid crystal spatial light modulator (SLM) to determine the zero-crossings. The zero-crossing SLM is a 32 X 32 array of pixels located on 100 micrometers centers. Each pixels contains a phototransistor, an auto-scaling amplifier, a zero-crossing detection circuit, and a liquid crystal modulating pad. Electrical and optical characteristics of the zero-crossing SLM are presented along with experimental results of the system.