Spectra computed from multispectral images of murine models of Rheumatoid Arthritis show a characteristic decrease in reflectance within the 600-800nm region which is indicative of the reduction in blood oxygenation and is consistent with hypoxia.
Plenoptic (light-field) imaging is a technique that allows a simple CCD-based imaging device to acquire both spatially and angularly resolved information about the “light-field” from a scene. It requires a microlens array to be placed between the objective lens and the sensor of the imaging device1 and the images under each microlens (which typically span many pixels) can be computationally post-processed to shift perspective, digital refocus, extend the depth of field, manipulate the aperture synthetically and generate a depth map from a single image. Some of these capabilities are rigid functions that do not depend upon the scene and work by manipulating and combining a well-defined set of pixels in the raw image. However, depth mapping requires specific features in the scene to be identified and registered between consecutive microimages. This process requires that the image has sufficient features for the registration, and in the absence of such features the algorithms become less reliable and incorrect depths are generated. The aim of this study is to investigate the generation of depth-maps from light-field images of scenes with insufficient features for accurate registration, using projected patterns to impose a texture on the scene that provides sufficient landmarks for the registration methods.
We present preliminary data from an imaging system based on LED illumination for obtaining sequential multispectral
optical images of the human ocular fundus. The system is capable of acquiring images at speeds of up
to 20fps and we have demonstrated that the system is fast enough to allow images to be acquired with minimal
inter-frame movement. Further improvements have been identified that will improve both imaging speed and
image quality. The long-term goal is to use the system in conjunction with novel image analysis algorithms to
extract chromophore concentrations from images of the ocular fundus, with a particular emphasis on age-related
macular degeneration. The system has also found utility in fluorescence microscopy.
One of the common physiological changes associated with cancer is the formation of a dense, irregular and leaky network of new blood vessels, which result in the increase of the blood volume fraction (BVF) at the site of a tumour. Such changes are not always obvious through visual inspection using a direct observation, an endoscopic device or colour photography. This paper presents a method for deriving quantitative estimates of BVF of the colon mucosa from multispectral images of the colon. The method has two stages. In the first ("forward") stage a physics-based model of light propagation computes the spectra corresponding to a range of instances of the colon tissue, and in particular the spectral changes resulting from changes in the quantity of blood volume fraction, haemoglobin saturation, the size and density of scattering particles, and the tissue thickness. In the second stage ("model inversion") the spectra obtained from the image data are used to derive the values of the above histological parameters. Parametric maps of the blood contents are created by storing at every pixel the BVF value recovered through the model inversion. In a pilot study multispectral images of ex-vivo samples of the colon were acquired from 8 patients. The samples contained histologically confirmed instances of adenocarcinoma and other pathologies. The parametric maps of BVF showed the significant increase in blood volume fraction (up to 75% above that of the surrounding the normal tissue). A Mann-Whitney test with Bonferroni correction showed that all but one of the differences (a benign neoplastic polyp) are significant (p<0.00015).
Colon cancer alters the tissue macro-architecture. Changes include increase in blood content and distortion of the collagen matrix, which affect the reflectance spectra of the colon and its colouration. We have developed a physics-based model for predicting colon tissue spectra. The colon structure is represented by three layers: mucosa, submucosa and smooth muscle. Each layer is represented by parameters defining its optical properties: molar concentration and absorption coefficients of haemoglobins, describing absorption of light; size and density of collagen fibres; refractive index of the medium and collagen fibres, describing light scattering; and layer thicknesses. Spectra were calculated using the Monte Carlo method. The output of the model was compared to experimental data comprising 50 spectra acquired in vivo from normal tissue. The extracted histological parameters showed good agreement with known values. An experiment was carried out to study the differences between normal and abnormal tissue. These were characterised by increased blood content and decreased collagen density, which is consistent with known differences between normal and abnormal tissue. This suggests that histological quantities of the colon could be computed from its reflectance spectra. The method is likely to have diagnostic value in the early detection of colon cancer.
Colon cancer alters the macroarchitecture of the colon tissue. Common changes include angiogenesis and the distortion of the tissue collagen matrix. Such changes affect the colon colouration. This paper presents the principles of a novel optical imaging method capable of extracting parameters depicting histological quantities of the colon. The method is based on a computational, physics-based model of light interaction with tissue. The colon structure is represented by three layers: mucosa, submucosa and muscle layer. Optical properties of the layers are defined by molar concentration and absorption coefficients of haemoglobins; the size and density of collagen fibres; the thickness of the layer and the refractive indexes of collagen and the medium. Using the entire histologically plausible ranges for these parameters, a cross-reference is created computationally between the histological quantities and the associated spectra. The output of the model was compared to experimental data acquired in vivo from 57 histologically confirmed normal and abnormal tissue samples and histological parameters were extracted. The model produced spectra which match well the measured data, with the corresponding spectral parameters being well within histologically plausible ranges. Parameters extracted for the abnormal spectra showed the increase in blood volume fraction and changes in collagen pattern characteristic of the colon cancer. The spectra extracted from multi-spectral images of ex-vivo colon including adenocarcinoma show the characteristic features associated with normal and abnormal colon tissue. These findings suggest that it should be possible to compute histological quantities for the colon from the multi-spectral images.
KEYWORDS: Data modeling, Tissues, Image filtering, Image processing, Tissue optics, Reflectivity, RGB color model, Optical filters, Monte Carlo methods, Cameras
Multi-spectral imaging of the ocular fundus suffers from three main problems: the image must be taken through an aperture (the pupil), meaning that the absolute light intensity at the fundus cannot be known; long acquisition times are not feasible due to patient discomfort; patient movement can lead to loss of image quality. These difficulties have meant that multi-spectral imaging of the fundus has not yet seen wide application. We have developed a new method for optimizing the multi-spectral imaging process which also allows us to derive semi-quantitative information about the structure and properties of the fundus. We acquire images in six visible spectral bands and use these to deduce the concentration and distribution of the known absorbing compounds in the fundus: blood haemoglobins in the retina and choroid, choroidal melanin, RPE melanin and xanthophyll. The optimisation process and parameter recovery uses a Monte Carlo model of the spectral reflectance of the fundus, parameterised by the concentrations of the absorbing compounds. The model is used to compute the accuracy with which the values of the model parameters can be deduced from an image. Filters are selected to minimise the error in the parameter recovery process. Theoretical investigations suggest that parameters can be recovered with RMS errors of less than 10%. When applied to images of normal subjects, the technique was able to successfully deduce the distribution of xanthophyll in the fundus. Further improvement of the model is required to allow the deduction of other model parameters from images.
A model of color formation within human skin has been developed to aid the characterization of pigmented skin lesions from their digitized color images. The model is based on the Kublenka-Munk theory of scattering and absorption within inhomogeneous materials and the physics pertaining to their color properties. By considering the skin to be a layered construction of such materials, the stratum corneum, epidermis, papillary dermis and reticular dermis, and by exploiting the physics related to the optical interface between these layers, the model generates all possible colors occurring within normal human skin. In particular, the model predicts that all skin colors have to lie on a simple curved surface patch within a three- dimensional color space bounded by two physiologically meaningful axes, one corresponding to the amount of melanin within the epidermis and the other to the amount of blood within the dermis. These predictions were verified by comparing the CIE LMS coordinates of a representative, cross-racial sample of fifty skin images with the LMS coordinates predicted by the model. The results show that, within the predicted error bounds, the coordinates for normal skin colors do indeed lie on the curved surface generated by the model. Several possible applications of this representation are outlined, including images representing the melanin and blood components separately, as well as the possibility of measuring the Breslow thickness of melanocytic invasion within malignant melanoma.
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