Fiber optic endomicroscopy is a valuable tool for clinical diagnostics and animal studies because it can capture images of tissue in vivo with subcellular resolution. Current configurations for endomicroscopes have either limited spatial resolution or require a scanning mechanism at the distal end of the fiber, which can slow imaging speed and increase the probe size. We present a novel configuration that provides high contrast 350×350 pixel images at 7.2 frames per second, without the need for mechanical scanning at the proximal or distal end of the fiber. The proof-of-concept benchtop system is tested in fluorescence mode and can resolve 1.5 µm features of a high resolution 1951 USAF target.
Image mapping spectrometry (IMS) is a hyperspectral imaging technique that simultaneously captures spatial and spectral information about an object in real-time. We present a new calibration procedure for the IMS as well as the first detailed evaluation of system performance. We correlate optical components and device calibration to performance metrics such as light throughput, scattered light, distortion, spectral image coregistration, and spatial/spectral resolution. Spectral sensitivity and motion artifacts are also evaluated with a dynamic biological experiment. The presented methodology of evaluation is useful in assessment of a variety of hyperspectral and multi-spectral modalities. Results are important to any potential users/developers of an IMS instrument and to anyone who may wish to compare the IMS to other imaging spectrometers.
This paper presents the Image Mapping Spectrometry a new snapshot hyperspectral imaging platform for variety of
applications. These applications span from remote sensing and surveillance use to life cell microscopy implementations
and medical diagnostics. The IMS replaces the camera in a digital imaging system, allowing one to add parallel spectrum
acquisition capability and to maximize the signal collection (> 80%). As such the IMS allows obtaining full spectral
information in the image scene instantaneously at real time imaging rates. Presented implemention provides 350x350x48
datacube (x,y,λ) and spectral sampling of 2 to 6 nm in visible spectral range but is easily expandable to larger cube
dimensions and other spectral ranges. The operation of the IMS is based on redirecting image zones through the use of a
custom-fabricated optical element known as an image mapper. The image mapper is a complex custom optical
component comprised of high quality, thin mirror facets with unique 2D tilts. These mirror facets reorganize the original
image onto a single large format CCD sensor to create optically "dark" regions between adjacent image lines. The full
spectrum from each image line is subsequently dispersed into the void regions on the CCD camera. This mapping
method provides a one-to-one correspondence between each voxel in the datacube and pixel on the CCD camera
requiring only a simple and fast remapping algorithm. This paper provides fundamentals of IMS operations and
describes an example design. Preliminary imaging results for gas detection acquired at 3 frames / second, for
350x350x48 data cubes are being presented. Real time unmixing of spectral signatures is also being discussed. Finally
paper draws perspective of future directions and system potential for infrared imaging.
Hyperspectral imaging has tremendous potential to detect important molecular biomarkers of early cancer based on their unique spectral signatures. Several drawbacks have limited its use for in vivo screening applications: most notably the poor temporal and spatial resolution, high expense, and low optical throughput of existing hyperspectral imagers. We present the development of a new real-time hyperspectral endoscope (called the image mapping spectroscopy endoscope) based on an image mapping technique capable of addressing these challenges. The parallel high throughput nature of this technique enables the device to operate at frame rates of 5.2 frames per second while collecting a (x, y, λ) datacube of 350 × 350 × 48. We have successfully imaged tissue in vivo, resolving a vasculature pattern of the lower lip while simultaneously detecting oxy-hemoglobin.
We present a rapid, noncontact imaging technique which can obtain the spectrally- and spatially-resolved scattering
and absorption coefficients of a turbid medium. The measurement involves combining a spatially modulated
illumination pattern with a snapshot imaging spectrometer for measurement. After capture of an (x, y, λ)
datacube, an image demodulation scheme is applied in post-processing to obtain the spatial maps of diffuse
reflectance, absorption coefficient, and reduced scattering coefficient. The resulting system is used to dynamic
maps (in 1 s intervals) of the brain's intrinsic optical signal.
Hyperspectral imaging has tremendous potential to detect important molecular biomarkers of early cancer based on their
unique spectral signatures. Several drawbacks have limited their use for in vivo screening applications: most notably
their poor temporal and spatial resolution, high expense, and low optical throughput. We present the development of a
new real-time hyperspectral endoscope (called the IMS Endoscope) based on an image mapping technique which makes
it capable of addressing these challenges. The parallel, high throughput nature of this technique enables the device to
operate at frame rates of 3-10 fps while collecting a 3D (x, y, λ) datacube of 350 x 350 x 48.
In this paper we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.