Near-infrared confocal microendoscopy is a promising technique for deep in vivo imaging of tissues and can generate high-resolution cross-sectional images at the micron-scale. We demonstrate the use of a dual-axis confocal (DAC) near-infrared fluorescence microendoscope with a 5.5-mm outer diameter for obtaining clinical images of human colorectal mucosa. High-speed two-dimensional en face scanning was achieved through a microelectromechanical systems (MEMS) scanner while a micromotor was used for adjusting the axial focus. In vivo images of human patients are collected at 5 frames/sec with a field of view of 362×212 μm2 and a maximum imaging depth of 140 μm. During routine endoscopy, indocyanine green (ICG) was topically applied a nonspecific optical contrasting agent to regions of the human colon. The DAC microendoscope was then used to obtain microanatomic images of the mucosa by detecting near-infrared fluorescence from ICG. These results suggest that DAC microendoscopy may have utility for visualizing the anatomical and, perhaps, functional changes associated with colorectal pathology for the early detection of colorectal cancer.
In recent years there has been growing interest in using confocal microscopy to observe tissue structure and function for
in vivo pathology. Although confocal microscopy can provide image resolution that is comparable to histopathology, it
can be limited by a small field-of-view as well as a low
signal-to-noise ratio. In this paper we show that image
mosaicing can enhance confocal microscopy by stitching multiple images together to widen the field-of-view and
increase the signal-to-noise ratio. Specifically, we present a
real-time image mosaicing system for imaging human skin
with a hand-held dual-axes confocal microscope. Our system allows the user to "paint" an image mosaic in real-time
and aids navigation by localizing the current view with respect to the larger image map. We first discuss image
calibration, then describe an efficient algorithm for real-time image mosaicing, and finally present experimental results
obtained in vivo with a dual-axes confocal microscope.
This work is part of an effort to develop smart composite materials that monitor their own health using embedded micro-sensors and local network communication nodes. Here we address the issue of data management through the development of localized processing algorithms. We demonstrate that the two-dimensional Fast Fourier Transform (FFT) is a useful algorithm due to its hierarchical structure and ability to determine the relative magnitudes of different spatial wavelengths in a material. We investigate different algorithms for implementing the distributed FFT and compare them in terms of computational requirements within a low-power, low-bandwidth network of microprocessors.