A multi-spectral imaging system is presented consisting of a spatially and spectrally uniform light source, an electronically tuneable bandpass filter and a sensitive digital camera. When properly calibrated with a white reference, it can reproducibly grab the reflection spectrum of any accessible tissue surface. After a description of technical challenges, some examples are given for the application in dermatology.

Detection and classification of fluorescent dyes are demonstrated using a computer screen photo-assisted technique (CSPT). This technique has previously been demonstrated for analyzing fluorescence from 96 wells microtiterplates (200 μl per well) and from a single cuvette with some optics to enhance sensitivity. In this work a custom designed array of wells with a volume of approximately 1 mu;l is used. In order to measure such small volumes without saturating the detector, the transmitted light is masked by placing the sample between two crossed polarizers. This arrangement blocks nearly all the transmitted light, while the emitted light, which is nearly unpolarized, can still be detected. The lowest amount (concentration x volume) of analyte detectable in this setup is about 40 times smaller than in the previous setups.

This paper describes a simple, inexpensive multispectral imaging system for image cytometry applications. The system is based on an acousto-optical tunable filter (AOTF), a monochrome CCD camera, and a research-grade fluorescence microscope. The selected TeO₂ AOTF has a 10x10 mm² entrance aperture and operates within the spectral range of 447-750 nm. The bandpass of the filter varies between 1.4 nm at 450 nm and 5.1 nm at 690 nm. The control software works within the environment of a popular image-acquisition and -processing package Image Pro-Plus, making this system easy to integrate with many existing fluorescence microscopes and cameras. Since image-cytometry applications do not require very high spatial resolution, AOTF-based systems may become an interesting alternative to more complex and expensive LCTF or pushbroom methods.

Advances in spectral imaging instrumentation during the last two decades has lead to higher image fidelity, tighter spatial resolution, narrower spectral resolution, and improved signal to noise ratios. An important sub-classification of spectral imaging is chemical imaging, in which the sought-after information from the sample is its chemical composition. Consequently, chemical imaging can be thought of as a two-step process, spectral image acquisition and the subsequent processing of the spectral image data to generate chemically relevant image contrast. While chemical imaging systems that provide turnkey data acquisition are increasingly widespread, better strategies to analyze the vast datasets they produce are needed. The Generation of chemically relevant image contrast from spectral image data requires multivariate processing algorithms that can categorize spectra according to shape. Conventional chemometric techniques like inverse least squares, classical least squares, multiple linear regression, principle component regression, and multivariate curve resolution are effective for predicting the chemical composition of samples having known constituents, but are less effective when a priori information about the sample is unavailable. To address these problems, we have developed a fully automated non-parametric technique called spectral identity mapping (SIMS) that reduces the dependence of spectral image analysis on training datasets. The qualitative SIMS method provides enhanced spectral shape specificity and improved chemical image contrast. We present SIMS results of infrared spectral image data acquired from polymer coated paper substrates used in the manufacture of pressure sensitive adhesive tapes. In addition, we compare the SIMS results to results from spectral angle mapping (SAM) and cosine correlation analysis (CCA), two closely related techniques.

We describe how spectral imaging, linear un-mixing and cluster computing have been combined to aid biomedical researchers and allow the spatial segmentation and quantitative analysis of immunohistochemically stained tissue section images. A novel cost-effective spectral imager, with a bandwidth of 15 nm between 400 and 700 nm, allows us to record both spatial and spectral data from absorptive and fluorescent chemical probes. The linear un-mixing of this data separates the stain distributions revealing areas of co-localisation and extracts quantitative values of optical density. This has been achieved at the single-pixel level of an image by non-negative least squares fitting. This process can be computationally expensive but great processing speed increases have been achieved through the use of cluster computing. We describe how several personal computers, running Microsoft WindowsXP, can be used in parallel, linked by the MPI (Message Passing Interface) standard. We describe how the free MPICH libraries have been incorporated into our spectral imaging application under the C language and how this has been extended to support features of MPI2 via the commercial WMPI II libraries. A cluster of 8 processors, in 4 dual-Athlon-2600+ computers, offered a speed up of a factor of 5 compared to a singleton. This includes the time required to transfer the data throughout the cluster and reflects a processing efficiency of 0.62 (a Cluster Efficacy of 3.0). The cluster was based on a 1000Base-T Ethernet network and appears to be scalable efficiently beyond 8 processors.

A problem of considerable interest in the hyperspectral and chemical imaging communities in recent years has been the automated identification and mapping of the constituent materials ("endmembers") present in a hyperspectral image. Several of the more important endmember-finding algorithms are discussed and some of their shortcomings highlighted. A relatively new algorithm, ICE, which attempts to address these shortcomings, is introduced. Although ICE was originally developed for exploration applications of airborne hyperspectral data, its performance on two biomedical data sets is investigated. Possible future research directions are outlined.

Tumor hypoxia has been shown to be of prognostic value in several clinical trials involving radiation, chemotherapy, and surgery. Studies of tumor oxygenation at the microvascular and microregional levels can provide understanding of tumor oxygen transport on scales comparable to the diffusion distance of oxygen in tissue. To fully grasp the significance of blood oxygen delivery and hypoxia at the microvascular level, the spatial and temporal relationship of blood oxygenation data must be preserved and mapped. Using tumors grown in dorsal skin-fold window chamber models, hyperspectral imaging can provide spatial maps of blood oxygenation in terms of hemoglobin saturation at the microvascular level, and these measurements can be performed serially in the same animal in a non-invasive fashion with relative technical ease. A hyperspectral imaging system has been constructed to create image maps of hemoglobin saturation in microvasculature of tumors grown in dorsal skin-fold window chambers. Preliminary baseline studies of early tumor development using 4T1 mouse mammary carcinomas are currently being conducted with the system.

The intense research in proteomics is demanding for fast, reliable and easy-to-use methods in order to study the proteome. In this proceeding we report the development of such a novel research tool based on spectral imaging and Resonance Light Scattering gold particles. This method will allow the study of DNA-protein interactions. We suggest a broad range of applications: the screening of proteins binding to a specific DNA sequence, the analysis of binding affinities between protein and DNA, and the investigation of the influence of environmental conditions on the binding. We will explain the principle, first experiments and first results based on Brownian motion.

Automated identification systems based on fingerprint images are subject to two significant types of error: an incorrect decision about the identity of a person due to a poor quality fingerprint image and incorrectly accepting a fingerprint image generated from an artificial sample or altered finger. This paper discusses the use of multispectral sensing as a means to collect additional information about a finger that significantly augments the information collected using a conventional fingerprint imager based on total internal reflectance. In the context of this paper, “multispectral sensing” is used broadly to denote a collection of images taken under different polarization conditions and illumination configurations, as well as using multiple wavelengths. Background information is provided on conventional fingerprint imaging. A multispectral imager for fingerprint imaging is then described and a means to combine the two imaging systems into a single unit is discussed. Results from an early-stage prototype of such a system are shown.

This paper describes a novel multispectral imaging microscope and its applications in the study of pollen grains in rice. The Imaging instruments can simultaneously record both spectral and spatial information of a sample, which is helpful to study the chemical states and physical properties of the sample by taking advantage of spatial image processing and spectroscopic analysis techniques. A LCTF (liquid crystal tunable filter) device is used for fast wavelength selection in the range of 400nm to 720nm and a cooled two-dimensional monochrome CCD for image detection. In this paper, the image acquisition process, spatial and spectral calibration and spectral imaging analysis methods are detailed. And also a novel method using this multispectral imaging microscope to observe rice pollen grains is reported here. The multispectral images were systematically processed and analyzed by the software. The results illustrated that the transmittance analysis of multispectral pollen images can accurately identify the pollen abortion stage of male-sterile rice, and can easily distinguish a variety of male sterile cytoplasm. Compared with cytological and histochemical methods reported previously, the method reported here has demonstrated to be more efficient and reliable in the study of chemical states and physical properties in plant cells.

We describe a new filter that simultaneously achieves spectral filtering and image replication to yield a two-dimensional, snapshot spectral imager. Filtering is achieved by spectral demultiplexing; that is without rejection of light; so optical throughput efficiency is, in principle, unity. The principle of operation can be considered as a generalisation of the Lyot filter to achieve multiple bandpasses. We report on the design and experimental implementation of an eight-band system for use in the visible. Proof-of-concept demonstrations are reported for imaging of the ocular fundus and microscopy of fluorescently labelled living cells.

We report on a volume holographic imaging spectrometer (VHIS) system which allows retrieval of a scene's two-dimensional spatial information as well as its spectral information. This is performed using a transmission volume hologram and a simple rotary scanning mechanism. The system has the advantages of high spectral and spatial resolutions and the potential of single-shot, four-dimensional (3D spatial plus 1D spectral) imaging by recording multiple volume holograms in the same material. Also, due to the transmission diffraction geometry, the system automatically eliminates the stray excitation light from the captured signal. We give theoretical analysis of the performance and experimental demonstration using fluorescent CdSe/ZeS quantum dots. The measured quantum dots spectra agree well with the spectra obtained using a conventional spectrometer.

Multiple methodologies exist to implement spectral imaging for tissue demarcation and disease diagnosis. In this paper, benchtop acousto-optic tunable filter (AOTF), liquid-crystal tunable filter (LCTF) and Fourier interferometric spectral imaging systems were quantitatively compared in terms of imaging speed of soft tissue autofluorescence. Optical throughput, image signal-to-noise ratio (SNR), and collagen autofluorescence imaging in chicken breast were assessed. Within this comparison, the Fourier system possessed the largest optical throughput (~50%) relative to the tunable-filter imaging systems; however, its throughput advantage failed to correlate to improved image SNR over the LCTF system. Further, while the autofluorescence imaging capability of the Fourier system exceeded that of the LCTF system for comparable total image integration times, the LCTF is capable of producing equivalent autofluorescence SNR with superior SNR when interrogations at only a few wavelengths are required and the random access filter tuning of the LCTF can be exploited. Therefore, the simple, rugged design and random-access filter-tuning capability of LCTF-based spectral imaging makes it best-suited for clinical development of soft tissue autofluorescence imaging.

We report a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile for hyperspectral, fluorescence, or principal component imaging. Most hyperspectral or multispectral imaging systems use wavelength selection devices such as acousto-optic tunable filters (AOTFs), tunable grating or prism-based monochromators, or filter wheels. While these devices can select wavelengths they cannot create arbitrary spectral profiles. This simple and economical system can be controlled at high speed (up to 5000 illumination profiles per second). Digitally controlled illumination is bit additive with image data providing high dynamic range imaging with monochrome or color imaging devices. This is especially advantageous for endoscopes employing small well CCD or CMOS sensors since the dynamic range now can extend beyond the limits of the sensor itself. In this report we show multispectral images of in vivo tissue and in vitro tissue samples using endoscopes, surgical microscopes and conventional microscopes.