Frame Accumulation Research Articles

Classification of Heterogeneity on Multi-Spectral Transmission Image Based on Modulation-Demodulation-Frame Accumulation and Pattern Recognition

Multi-spectral transmission image provides a possibility for the detection of early breast cancer. However, in the process of acquiring multi-spectral transmission images, the recognition of heterogeneities has many difficulties due to the image blur caused by the scattering effect of light source in biological tissues and weak transmission signals. This paper proposes a combination method of modulation–demodulation-frame accumulation technique and pattern recognition to achieve heterogeneous classification. First, the acquisition experiment of the phantom multi-spectral images is designed. Then, the signal-to-noise ratio (SNR) of the image is improved by the modulation–demodulation and frame accumulation technique, and the 14-dimensional feature information (firmness, angular second-order distance, contrast, gray-scale correlation, entropy, inverse gap, smoothness, dissimilarity, consistency, center of gravity, area, perimeter, long diameter of irregular image, and short diameter of irregular image) of the heterogeneous region are extracted from the image with high SNR. Finally, the heterogeneous classification accuracy of different models is compared. The results show that: compared with the classification accuracy of the traditional multi-spectral image classification models, random forest (RF) and extreme learning machine (ELM) models have better classification effect when subdividing the four types of heterogeneity based on the data set of this paper. Among them, the RF and ELM models established by the dataset of four-wavelength combination have the best classification effect, and the classification accuracy rate reaches 100%, second, it is the three-wavelength combined model. The single-wavelength model has the worst classification effect. And the operating efficiency of ELM is significantly higher than RF. In conclusion, the image quality is improved by modulation–demodulation and frame accumulation technique. And compared with the classification accuracy of the traditional multi-spectral image classification models, the RF and ELM models established in this paper have better classification effect, which may promote the application of multi-spectral transmission imaging in early screening of breast tumors.

A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image

Multi-wavelength transmission imaging provides a possibility for early breast cancer detection. However, due to the strong scattering effect of light source in the transmission process of biological tissue, there exist many difficulties in the heterogeneous identification of multi-wavelength images. In this paper, a multi-wavelength images frequency fusion method based on regional variance significance and regional space energy weighting is proposed to improve the accuracy of heterogeneous detection. Firstly, the acquisition experiment of phantom multi-wavelength transmission images is designed. And R\G\B three-channel wavelength images are extracted from multi-wavelength images as an example to realize subsequent image fusion. Secondly, frame accumulation technique and second-order Butterworth filter are used to improve the signal-to-noise ratio of images and suppress the noise of images. Then, after homomorphic filtering, it is found that the same frequency domain of images is connected, which is not conducive to the recognition of heterogeneity in image. In order to break the connectivity of images in the same frequency domain and improve the accuracy of heterogeneous detection, the regional variance significance and the regional space energy weighting fusion strategies are used to reconstruct the low-frequency and high-frequency fusion images respectively. Finally, the heterogeneous detection results of the proposed image fusion method and the state-of-the-art methods in the multi-wavelength image are compared. The experimental results show that compared with the state-of-the-art image fusion methods, the fusion method proposed in this paper can identify the location of heterogeneous region more accurately and significantly improve the contrast between heterogeneous region and normal region in the fusion image. In conclusion, the fusion algorithm not only effectively realizes the recognition of heterogeneity in multi-wavelength images, but also promotes the potential application of multi-wavelength transmission image in early breast cancer detection.

Improved method of frame accumulation technology using dual optical signals

The traditional frame accumulation technology can effectively reduce the random noise and improve the signal-to-noise ratio in the image. A frame accumulation technology with superimposed sawtooth-shaped-function can reduce the quantization noise and increase the grayscale resolution. However, in the case of continuous operating environment, it is not suitable because constant amplitude light used must be turned off for taking pure and shadowy images, which are generated by superimposed shaped light. To solve this problem, we propose an improved method named dual optical signals with different periods (DOSDP) to improve grayscale resolution of an image. The method does not need to capture signals twice. In other situations, where it is not appropriate to directly superimpose a shaped function optical signal to an object, a piece of glass is installed on the light path between the camera and the object so that the superimposed light can enter the camera through the glass reflection. Furthermore, our method allows superimposed signals to be nonlinear. Experimental results show that DOSDP method can effectively improve quality of the image.

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.

A protocol for registration and correction of multicolour STED superresolution images.

Multicolour fluorescence imaging by STimulated Emission Depletion (STED) superresolution microscopy with doughnut-shaped STED laser beams based on different wavelengths for each colour channel requires precise image registration. This is especially important when STED imaging is used for co-localisation studies of two or more native proteins in biological specimens to analyse nanometric subcellular spatial arrangements. We developed a robust postprocessing image registration protocol, with the aim to verify and ultimately optimise multicolour STED image quality. Importantly, this protocol will support any subsequent quantitative localisation analysis at nanometric scales. Henceforth, using an approach that registers each colour channel present during STED imaging individually, this protocol reliably corrects for optical aberrations and inadvertent sample drift. To achieve the latter goal, the protocol combines the experimental sample information, from corresponding STED and confocal images using the same optical beam path and setup, with that of an independent calibration sample. As a result, image registration is based on a strategy that maximises the cross-correlation between sequentially acquired images of the experimental sample, which are strategically combined by the protocol. We demonstrate the general applicability of the image registration protocol by co-staining of the ryanodine receptor calcium release channel in primary mouse cardiomyocytes. To validate this new approach, we identify user-friendly criteria, which - if fulfilled - support optimal image registration. In summary, we introduce a new method for image registration and rationally based postprocessing steps through a highly standardised protocol for multicolour STED imaging, which directly supports the reproducibility of protein co-localisation analyses. Although the reference protocol is discussed exemplarily for two-colour STED imaging, it can be readily expanded to three or more colours and STED channels.

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.

Optimum method of image acquisition using sawtooth-shaped-function optical signal to improve grey-scale resolution

The subtle changes in the images are determined to some extent by the grey-scale resolution. This paper presents a method used for optimizing the grey-scale resolution of the acquired images. The optical source is modulated by the sawtooth-shaped-function signal, and the acquired images by the sensor are processed by the frame accumulation technology. Compared to the traditional method using the optical source with constant intensity, this method can greatly improve the grey-scale resolution of images by more than 1 bit under the condition of low energy light. Based on the method, we can not only obtain higher quality images but also conserve energy, which is very important in the field of fluorescence imaging, biomedical imaging, face recognition and security system.

Field programmable gate array processing for an improved low-light-level imaging system with higher detection sensibility.

The method which employs the frame accumulation and shaped function is effective in low-light-level imaging. However, it has drawbacks of lower imaging speed and complex operation. To optimize the method, we provide the design of an improved low-light-level imaging system with higher detection sensibility. The design is developed specifically for a faster imaging speed based on field programmable gate arrays. It features the use of least-square algorithm and a saw-tooth wave varied light applied to the image sensor. By manipulation of the video signal in synchronous dynamic random access memory, a low-light-level image which was previously undetectable can be estimated. The design simplifies the imaging process and doubles the imaging speed, and makes the system adapted to long range imaging.

Employment of frame accumulation and shaped function for upgrading low-light-level image detection sensitivity

We demonstrate a novel method that employs a frame-accumulation and shaped-function technique to improve an image sensor's detection sensitivity using probability statistics. It can obtain the unbiased estimate for the low-light-level image signal, thus upgrading the signal-to-noise ratio to a high degree. It was verified by an experiment in a sealed box. By the help of a variable light beam-shaped function saw-tooth signal in front of a camera, the low-light-level shadow image that was invisible to the camera could be revealed clearly from the frame accumulation data. The method can surpass an image sensor's detection limitation.