Multi-spectral imaging provides digital images of a scene or object at a large, usually sequential number of wavelengths,
generating precise optical spectra at every pixel. We use the term "spectral signature" for a quantitative plot of optical
property variations as a function of wavelengths. We present here intelligent spectral signature bio-imaging methods we
developed, including automatic signature selection based on machine learning algorithms and database search-based
automatic color allocations, and selected visualization schemes matching these approaches. Using this intelligent spectral
signature bio-imaging method, we could discriminate normal and aganglionic colon tissue of the Hirschsprung's disease
mouse model with over 95% sensitivity and specificity in various similarity measure methods and various anatomic
organs such as parathyroid gland, thyroid gland and pre-tracheal fat in dissected neck of the rat in vivo.
Spectral imaging has recently been introduced in the biomedical field as a noninvasive, quantitative means of studying biological tissues. Many of its potential applications have been demonstrated (in vitro and, to a lesser degree, in vivo) with the use of stains or dyes. Successful translation to the clinical environment has been largely lagging, due to safety considerations and regulatory limitations preventing use of contrast agents in humans. We report experiments showing the feasibility of high-resolution spectral imaging of breast cancer without the use of contrast agents, thus completing the continuum of translational research, to in vivo imaging that will be directly applicable in the clinical environment. Our initial work focused on image acquisition using Fourier transform microinterferometry and subsequent segmentation of both stained and unstained breast cancer slides-derived image sets. We then applied our techniques to imaging fresh unstained ex vivo specimens of rat breast cancer and sentinel lymph nodes. We also investigated multiple methods of classification to optimize our image analyses, and preliminary results for the best algorithm tested yielded an overall sensitivity of 96%, and a specificity of 92% for cancer detection. Using spectral imaging and classification techniques, we were able to demonstrate that reliable detection of breast cancer in fixed and fresh unstained specimens of breast tissue is possible.
Biological specimens are three-dimensional structures. However, when capturing their images through a microscope, there is only one plane in the field of view that is in focus, and out-of-focus portions of the specimen affect image quality in the in-focus plane. It is well-established that the microscope’s point spread function (PSF) can be used for blur quantitation, for the restoration of real images. However, this is an ill-posed problem, with no unique solution and with high computational complexity. In this work, instead of estimating and using the PSF, we studied focus quantitation in
multi-spectral image sets. A gradient map we designed was used to evaluate the sharpness degree of each pixel, in order to identify blurred areas not to be considered. Experiments with realistic multi-spectral Pap smear images showed that measurement of their sharp gradients can provide depth information roughly comparable to human perception (through a microscope), while avoiding PSF estimation. Spectrum and morphometrics-based statistical analysis for abnormal cell detection can then be implemented in an image database where the axial structure has been refined.