The accurate prediction of spectral sensitivity of digital camera is essential for various aspects in color science, such as color correction, color rendering and color constancy. In this paper, a multi-objective optimization algorithm was proposed to estimate the spectral sensitivity of cameras. Multiple objective functions and Sine subspace based spectral sensitivity were employed in the proposed algorithm, in which excellent robustness and high smoothness were achieved. The performance of this algorithm was theoretically evaluated by multiple numerical simulation experiments, and was further compared with other algorithms in previous literatures based on the criteria of color aberration (δE), spectral recovery error (SE) and similarity between the estimated sensors and the measured ground truth (Vora). According to the numerical simulation results, the multi-objective algorithm can significantly improve the performance of the spectral sensitivity estimation, which may promote its various applications in the fields of color correction and illumination modeling between cameras.
Significance: Hyperspectral microscopy has been intensively explored in biomedical applications. However, due to its huge three-dimensional hyperspectral data cube, it typically suffers from slow data acquisition, mass data transmission and storage, and computationally expensive postprocessing.
Aim: To overcome the above limitations, a programmable hyperspectral microscopy technique was developed, which can perform hardware-based hyperspectral data postprocessing by the physical process of optical imaging in a snapshot.
Approach: A programmable hyperspectral microscopy system was developed to collect coded microscopic images from samples under multiplexed illumination. Principal component analysis followed by linear discriminant analysis scheme was coded into multiplexed illumination and realized by the physical process of optical imaging. The contrast enhancement was evaluated on two representative types of microscopic samples, i.e., tissue section and cell samples.
Results: Compared to the microscopic images collected under white light illumination, the contrasts of coded microscopic images were significantly improved by 41% and 59% for tissue section and cell samples, respectively.
Conclusions: The proposed method can perform hyperspectral data acquisition and postprocessing simultaneously by its physical process, while preserving the most important spectral information to maximize the difference between the target and background, thus opening a new avenue for high-contrast microscopic imaging in a snapshot.
Cryptococcus neoformans is an encapsulated fungus that widely exists in the environment through the world. It can enter the human body through the respiratory tract, causing inflammation of the lungs and even causing serious infectious diseases in patients with impaired immune function. Those serious infectious diseases can be avoided if the cryptococcal pneumonia can be early detected and accurately assessed. However, due to less clinical symptoms, cryptococcal pneumonia is easily misdiagnosed as lung cancer, tuberculosis, etc., and the rate of misdiagnosis is high. Therefore, a rapid, accurate and non-invasive method is urgently needed to make early diagnosis and precise assessment of cryptococcal pneumonia, in which timely and appropriate treatment can be implemented and the cure rate is expected to be significantly improved. In this study, surface enhanced Raman spectroscopy (SERS) was served as the diagnostic tool to identify cryptococcal pneumonia, in which the blood serum SERS spectra are collected from cryptococcal pneumonia mice and uninfected mice. The multivariate curve resolution alternating least squares (MCR-ALS) and support vector machine (SVM) are employed to quantify the biochemical composition changes in serum microenvironments. The excellent results have demonstrated that the serum SERS has significant potential in early diagnosis and accurate assessment of cryptococcal pneumonia.
Biprism-based monocular stereovision systems can acquire different views of the same object at a single shot, which have several advantages over conventional two-camera systems. To measure the position or recover the shape of the object, effective image pairs have to be captured first, which depends on the field of view of the system. In this paper, we propose a common method for establishing a practical biprism-based monocular stereovision system. The relationship between system parameters and object distance has been analyzed in details. The criterion for parameter optimization and procedure for system setup have been introduced. Experimental results show that our method can effectively calculate different conditions under different parameters and optimize the parameter configurations for the particular system.
The speed of data acquisition is a major hurdle for hyperspectral spontaneous Raman imaging to be widely adopted in the clinical setting. To address this problem, we proposed a new approach to achieve fast spectroscopic imaging while keeping high spectral resolution, in which narrow-band or wide-band imaging quickly captures all required data and then full spectra at all pixels are reconstructed efficiently. We started by developing a method to enable the reconstruction of diffuse reflectance spectra from color images with high accuracy. This method was further developed for hyperspectral Raman imaging from narrow-band measurements. Then a series of Wiener estimation based methods were developed to improve the accuracy of spectral reconstruction and reduce the need of acquiring a training dataset. A four-channel Raman imaging system has been built to acquire all narrow-band images in one single frame and an eight-channel imaging system is currently under evaluation. This technique could speed up the acquisition of hyperspectral data cube by two to three orders of magnitude, which opens the possibility of rapid Raman imaging for the monitoring of dynamically changing events in biological samples. Moreover, other hyperspectral imaging modalities including diffuse reflectance and fluorescence imaging can also benefit from this fast spectroscopic imaging technique, which have been demonstrated in flap assessment during plastic surgery on an animal model.
Raman spectroscopy has demonstrated great potential in biomedical applications. However, spectroscopic Raman imaging is not widely used because of slow data acquisition. Our previous studies have indicated that spectroscopic Raman imaging can be significantly sped up using the approach of narrow-band imaging followed by spectral reconstruction. A multi-channel system has been built to demonstrate the feasibility of fast wide-field Raman spectroscopic imaging based on simultaneous narrow-band image acquisition and spectral reconstruction based on Wiener estimation in phantoms. To further improve the accuracy of reconstructed Raman spectra, we propose a stepwise spectral reconstruction method in this study, which can be combined with the earlier developed sequential weighted Wiener estimation to improve the spectral reconstruction accuracy. The stepwise spectral reconstruction method first reconstructs the fluorescence background spectra from narrow-band measurements by sequential weighted Wiener estimation and then the pure Raman narrow-band measurements can be estimated by subtracting the estimated fluorescence background from the overall Raman measurements. Thereafter, pure Raman spectra can be reconstructed from estimated pure Raman narrow-band measurements. The result indicates that the stepwise spectral reconstruction method can improve the spectral reconstruction accuracy by more than 30% when combined with sequential weighted Wiener estimation, compared with traditional Wiener estimation. In addition, cell Raman imaging were realized by using a multi-channel wide field Raman spectroscopic imaging and the stepwise spectral reconstruction method. This method can potentially facilitate the use of spectroscopic Raman imaging to investigate fast changing phenomena in biological samples.
Key tissue parameters, e.g., total hemoglobin concentration and tissue oxygenation, are important biomarkers in clinical diagnosis for various diseases. Although point measurement techniques based on diffuse reflectance spectroscopy can accurately recover these tissue parameters, they are not suitable for the examination of a large tissue region due to slow data acquisition. The previous imaging studies have shown that hemoglobin concentration and oxygenation can be estimated from color measurements with the assumption of known scattering properties, which is impractical in clinical applications. To overcome this limitation and speed-up image processing, we propose a method of sequential weighted Wiener estimation (WE) to quickly extract key tissue parameters, including total hemoglobin concentration (CtHb), hemoglobin oxygenation (StO2), scatterer density (α), and scattering power (β), from wide-band color measurements. This method takes advantage of the fact that each parameter is sensitive to the color measurements in a different way and attempts to maximize the contribution of those color measurements likely to generate correct results in WE. The method was evaluated on skin phantoms with varying CtHb, StO2, and scattering properties. The results demonstrate excellent agreement between the estimated tissue parameters and the corresponding reference values. Compared with traditional WE, the sequential weighted WE shows significant improvement in the estimation accuracy. This method could be used to monitor tissue parameters in an imaging setup in real time.
The accurate assessment of skin flap viability is vitally important in reconstructive surgery. Early identification of
vascular compromise increases the change of successful flap salvage. The ability to determine tissue viability intraoperatively
is also extremely useful when the reconstructive surgeon must decide how to inset the flap and whether any
tissue must be discarded. Visible diffuse reflectance and auto-fluorescence spectroscopy, which yield different sets of
biochemical information, have not been used in the characterization of skin flap viability simultaneously to our best
knowledge. We performed both diffuse reflectance and fluorescence measurements on a reverse MacFarlane rat dorsal
skin flap model to identify the additional value of auto-fluorescence spectroscopy to the assessment of flap viability. Our
result suggests that auto-fluorescence spectroscopy appears to be more sensitive to early biochemical changes in a failed
flap than diffuse reflectance spectroscopy, which could be a valuable complement to diffuse reflectance spectroscopy for
the assessment of flap viability.
Raman spectroscopy has demonstrated great potential in the study of biological molecules in a variety of biomedical
applications. But slow data acquisition due to weak Raman signals from these molecules has prevented its wide use
especially in an imaging setup. We propose a novel method to reconstruct the entire Raman spectrum from a few narrow-band
measurements based on Wiener estimation. This method has been tested on Raman spectra from individual cells
and shown fast speed and excellent accuracy. This method represents a new direction to speed up Raman data acquisition
in an imaging setup to investigate fast changing phenomena.
We present a new method for the accurate estimation of diffuse reflectance spectra from RGB values based on Wiener estimation. In the proposed method, a system matrix obtained from the original RGB values is combined with a set of synthetic optical filters to generate another three values corresponding to new colors. A modified Wiener matrix can then be created with the original RGB values and the new color values, which will yield a more accurate estimation because of the new color information that has been incorporated. This method was tested on in vivo color measurements from 200 skin sites in 10 volunteers. The results show that the proposed method is able to improve the estimation accuracy significantly compared with the traditional Wiener estimation method. The fast speed of this method may enable the estimation of diffuse reflectance spectra at multiple tissue locations from color images in real time, which provides a cost-effective alternative to spectral imaging with the additional advantage of high spectral resolution.
A modified Wiener estimation method is presented for the accurate estimation of diffuse reflectance spectra from RGB
values. In this method, the original RGB values are combined with a set of synthetic optical filters to generate another
new three color values by using the system matrix. A modified Wiener matrix can then be created with the RGB values
and the new color values, which will yield more accurate estimation because of the new color information incorporated.
This method is tested on in-vivo color measurements from 200 skin sites in 10 volunteers. The results show that the
proposed method improves the accuracy of the estimated diffuse reflectance spectra significantly compared with the
traditional Wiener estimation method. Because of the fast computation in Wiener estimation, this method could be
potentially developed for a cost effective alternative to a spectral imager.
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