Optimum method of applying and removing a shaped-function signal for low-light-level image detection

Heterogeneity Detection Method for Transmission Multispectral Imaging Based on Contour and Spectral Features

Transmission multispectral imaging (TMI) has potential value for medical applications, such as early screening for breast cancer. However, because biological tissue has strong scattering and absorption characteristics, the heterogeneity detection capability of TMI is poor. Many techniques, such as frame accumulation and shape function signal modulation/demodulation techniques, can improve detection accuracy. In this work, we develop a heterogeneity detection method by combining the contour features and spectral features of TMI. Firstly, the acquisition experiment of the phantom multispectral images was designed. Secondly, the signal-to-noise ratio (SNR) and grayscale level were improved by combining frame accumulation with shape function signal modulation and demodulation techniques. Then, an image exponential downsampling pyramid and Laplace operator were used to roughly extract and fuse the contours of all heterogeneities in images produced by a variety of wavelengths. Finally, we used the hypothesis of invariant parameters to do heterogeneity classification. Experimental results show that these invariant parameters can effectively distinguish heterogeneities with various thicknesses. Moreover, this method may provide a reference for heterogeneity detection in TMI.

Optimized lighting method of applying shaped-function signal for increasing the dynamic range of LED-multispectral imaging system

This paper proposes an optimized lighting method of applying a shaped-function signal for increasing the dynamic range of light emitting diode (LED)-multispectral imaging system. The optimized lighting method is based on the linear response zone of the analog-to-digital conversion (ADC) and the spectral response of the camera. The auxiliary light at a higher sensitivity-camera area is introduced to increase the A/D quantization levels that are within the linear response zone of ADC and improve the signal-to-noise ratio. The active light is modulated by the shaped-function signal to improve the gray-scale resolution of the image. And the auxiliary light is modulated by the constant intensity signal, which is easy to acquire the images under the active light irradiation. The least square method is employed to precisely extract the desired images. One wavelength in multispectral imaging based on LED illumination was taken as an example. It has been proven by experiments that the gray-scale resolution and the accuracy of information of the images acquired by the proposed method were both significantly improved. The optimum method opens up avenues for the hyperspectral imaging of biological tissue.

Image quality assessment metric for frame accumulated image

The medical image quality determines the accuracy of diagnosis, and the gray-scale resolution is an important parameter to measure image quality. But current objective metrics are not very suitable for assessing medical images obtained by frame accumulation technology. Little attention was paid to the gray-scale resolution, basically based on spatial resolution and limited to the 256 level gray scale of the existing display device. Thus, this paper proposes a metric, "mean signal-to-noise ratio" (MSNR) based on signal-to-noise in order to be more reasonable to evaluate frame accumulated medical image quality. We demonstrate its potential application through a series of images under a constant illumination signal. Here, the mean image of enough images was regarded as the reference image. Several groups of images by different frame accumulation and their MSNR were calculated. The results of the experiment show that, compared with other quality assessment methods, the metric is simpler, more effective, and more suitable for assessing frame accumulated images that surpass the gray scale and precision of the original image.

An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal

The oxygen saturation is one of the important parameters to evaluate human health. This paper presents an efficient optimization method that can improve the accuracy of oxygen saturation measurement, which employs an optical frequency division triangular wave signal as the excitation signal to obtain dynamic spectrum and calculate oxygen saturation. In comparison to the traditional method measured RMSE (root mean square error) of SpO₂ which is 0.1705, this proposed method significantly reduced the measured RMSE which is 0.0965. It is notable that the accuracy of oxygen saturation measurement has been improved significantly. The method can simplify the circuit and bring down the demand of elements. Furthermore, it has a great reference value on improving the signal to noise ratio of other physiological signals.

Effect on measurement accuracy of transillumination using sawtooth-shaped-function optical signal

There exist lots of difficulties in optical transillumination of biological tissues, especially, low-level-light detection and low gray-scale resolution could reduce recognition accuracy of differences in tissue. Aiming at the problems, this paper proposes a method for optical transillumination using sawtooth-shaped-function optical signal and frame accumulation technology to explore the spatial information of heterogeneity in the tissue. Sawtooth-shaped-function optical signal combined with frame accumulation technology is applied to realize the low-level-light image detection, where frame accumulation technology improves the signal-to-noise ratio and detection sensitivity, and sawtooth-shaped-function signal improves gray-scale resolution. The results of the experiment applied on the flat-shaped phantom demonstrated that, compared with the constant intensity optical signal, the application of the sawtooth-shaped-function optical signal effectively improved the gray-scale resolution, thus improved the extraction accuracy on positional feature pixels of heterogeneity. In addition, the sawtooth-shaped-function optical signal reduces the light radiation per unit time and per unit area. Therefore, the proposed illuminated way is more suitable for the imaging of biological tissue.

Employment of sawtooth-shaped-function excitation signal and oversampling for improving resistance measurement accuracy

In order to achieve higher measurement accuracy of routine resistance without increasing the complexity and cost of the system circuit of existing methods, this paper presents a novel method that exploits a shaped-function excitation signal and oversampling technology. The excitation signal source for resistance measurement is modulated by the sawtooth-shaped-function signal, and oversampling technology is employed to increase the resolution and the accuracy of the measurement system. Compared with the traditional method of using constant amplitude excitation signal, this method can effectively enhance the measuring accuracy by almost one order of magnitude and reduce the root mean square error by 3.75 times under the same measurement conditions. The results of experiments show that the novel method can attain the aim of significantly improve the measurement accuracy of resistance on the premise of not increasing the system cost and complexity of the circuit, which is significantly valuable for applying in electronic instruments.

Local Sparse Structure Denoising for Low-Light-Level Image

Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.