Wavelet-based speckle noise reduction in ultrasound B-scan images

Speckle noise is known to be signal-dependent in ultrasound imaging. Hence, separating noise from signal becomes a difficult task. This paper describes a wavelet-based method for reducing speckle noise. We derive from the model of the displayed ultrasound image the optimal wavelet-domain filter in the least mean-square sense. Simulations on synthetic data have been carried out in order to assess the performance of the proposed filter with regards to the classical wavelet shrinkage scheme, while phantom and tissue images have been used for testing it on real data. The results show that the filter effectively reduces the speckle noise while preserving resolvable details.     

基于小波的磁共振图像聚类分割

目的 利用直方图自适应确定人体不同部位MRI的聚类类别的数目和相应的初始聚类中心,实现模糊-c均值聚类算法(FCM)分割的自适应。方法 首先采用小波变换拟合直方图的平滑包络线,降低噪声对寻找包络线极值的影响;其次根据微积分的知识求出包络线极大值的个数,按照文中给出的法则对包络线的极大值进行筛选,确定直方图中峰值的个数;最后以直方图中峰值的个数为聚类类别数,以相应的峰值为初始聚类中心,对MRI进行FCM分割。结果 采用该方法对多幅腹部和脑部MR图像进行分割,均能有效地自适应确定聚类的个数。结论 本文方法能够有效、准确地确定不同MR图像的聚类类别的个数,实现FCM的自适应。     

Deep probabilistic tracking of particles in fluorescence microscopy images

Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods.     

Wavelet-based edge detection in ultrasound images

We introduce a new wavelet-based method for edge detection in ultrasound (US) images. Each beam that is analyzed is first transformed into the wavelet domain using the continuous wavelet transform (CWT). Because the CWT preserves both scale and time information, it is possible to separate the signal into a number of scales. The edge is localized by first determining the scale at which the power spectrum, based on the wavelet transform, has its maximum value. Next, at this scale we find the position of the peak for the squared CWT. This method does not depend on any threshold, after the range of scales have been determined. We suggest a range of scales for US images in general. Sample edge detections are demonstrated in US images of straight and jagged edges of simple structures submerged in water bath, and of an abdominal aorta aneurysm phantom.     

Data fusion and smoothing for probabilistic tracking of viral structures in fluorescence microscopy images

Automatic tracking of viral structures displayed as small spots in fluorescence microscopy images is an important task to determine quantitative information about cellular processes. We introduce a novel probabilistic approach for tracking multiple particles based on multi-sensor data fusion and Bayesian smoothing methods. The approach exploits multiple measurements as in a particle filter, both detection-based measurements and prediction-based measurements from a Kalman filter using probabilistic data association with elliptical sampling. Compared to previous probabilistic tracking methods, our approach exploits separate uncertainties for the detection-based and prediction-based measurements, and integrates them by a sequential multi-sensor data fusion method. In addition, information from both past and future time points is taken into account by a Bayesian smoothing method in conjunction with the covariance intersection algorithm for data fusion. Also, motion information based on displacements is used to improve correspondence finding. Our approach has been evaluated on data of the Particle Tracking Challenge and yielded state-of-the-art results or outperformed previous approaches. We also applied our approach to challenging time-lapse fluorescence microscopy data of human immunodeficiency virus type 1 and hepatitis C virus proteins acquired with different types of microscopes and spatial-temporal resolutions. It turned out, that our approach outperforms existing methods.     

Dynamic real-time subtraction of stray-light and background for multiphoton imaging

We introduce a new approach to reduce uncorrelated background signals from fluorescence imaging data, using real-time subtraction of background light. This approach takes advantage of the short fluorescence lifetime of most popular fluorescent activity reporters, and the low duty-cycle of ultrafast lasers. By synchronizing excitation and recording, laser-induced multiphoton fluorescence can be discriminated from background light levels with each laser pulse. We demonstrate the ability of our method to – in real-time – remove image artifacts that in a conventional imaging setup lead to clipping of the signal. In other words, our method enables imaging under conditions that in a conventional setup would yield corrupted data from which no accurate information can be extracted. This is advantageous in experimental setups requiring additional light sources for applications such as optogenetic stimulation.     

Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks

Contrast in fluorescence microscopy images allows for the differentiation between different structures by their difference in intensities. However, factors such as point-spread function and noise may reduce it, affecting its interpretability. We identified that fluctuation of emitters in a stack of images can be exploited to achieve increased contrast when compared to the average and Richardson-Lucy deconvolution. We tested our methods on four increasingly challenging samples including tissue, in which case results were comparable to the ones obtained by structured illumination microscopy in terms of contrast.     

Robust Rician noise estimation for MR images

In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.     

Miniaturized modular-array fluorescence microscopy

Fluorescence live-cell imaging allows for continuous interrogation of cellular behaviors, and the recent development of portable live-cell imaging platforms has rapidly transformed conventional schemes with high adaptability, cost-effective functionalities and easy accessibility to cell-based assays. However, broader applications remain restrictive due to compatibility with conventional cell culture workflow and biochemical sensors, accessibility to up-right physiological imaging, or parallelization of data acquisition. Here, we introduce miniaturized modular-array fluorescence microscopy (MAM) for compact live-cell imaging in flexible formats. We advance the current miniscopy technology to devise an up-right modular architecture, each combining a gradient-index (GRIN) objective and individually-addressed illumination and acquisition components. Parallelization of an array of such modular devices allows for multi-site data acquisition in situ using conventional off-the-shelf cell chambers. Compared with existing methods, the device offers a high fluorescence sensitivity and efficiency, exquisite spatiotemporal resolution (∼3 µm and up to 60 Hz), a configuration compatible with conventional cell culture assays and physiological imaging, and an effective parallelization of data acquisition. The system has been demonstrated using various calibration and biological samples and experimental conditions, representing a promising solution to time-lapse in situ single-cell imaging and analysis.     

Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals

In high-throughput applications, accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression and the understanding of cell function. We propose an approach for segmenting cell nuclei which is based on active contours using level sets and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We consider three different well-known energy functionals for active contour-based segmentation and introduce convex formulations of these functionals. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images from different experiments comprising different cell types. We have also performed a quantitative comparison with previous segmentation approaches.     

Frequency domain photoacoustic and fluorescence microscopy

We report on simultaneous frequency domain optical-resolution photoacoustic and fluorescence microscopy with sub-µm lateral resolution. With the help of a blood smear, we show that photoacoustic and fluorescence images provide complementary information. Furthermore, we compare theoretically predicted signal-to-noise ratios of sinusoidal modulation in frequency domain with pulsed excitation in time domain.OCIS codes: (170.5120) Photoacoustic imaging, (180.2520) Fluorescence microscopy, (110.5125) Photoacoustics, (170.3880) Medical and biological imaging, (140.2020) Diode lasers     

Development of Trivia Game for speech understanding in background noise

Abstract

Purpose: Listening in noise is an everyday activity and poses a challenge for many people. To improve the ability to understand speech in noise, a computerized auditory rehabilitation game was developed. In Trivia Game players are challenged to answer trivia questions spoken aloud. As players progress through the game, the level of background noise increases. A study using Trivia Game was conducted as a proof-of-concept investigation in healthy participants.

Method: College students with normal hearing were randomly assigned to a control (n = 13) or a treatment (n = 14) group. Treatment participants played Trivia Game 12 times over a 4-week period. All participants completed objective (auditory-only and audiovisual formats) and subjective listening in noise measures at baseline and 4 weeks later.

Result: There were no statistical differences between the groups at baseline. At post-test, the treatment group significantly improved their overall speech understanding in noise in the audiovisual condition and reported significant benefits in their functional listening abilities.

Conclusion: Playing Trivia Game improved speech understanding in noise in healthy listeners. Significant findings for the audiovisual condition suggest that participants improved face-reading abilities. Trivia Game may be a platform for investigating changes in speech understanding in individuals with sensory, linguistic and cognitive impairments.     

Limited-angle tomographic phase microscopy utilizing confocal scanning fluorescence microscopy

We present a multimodal imaging technique, combining tomographic phase microscopy with limited angular projection range and number, and two-channel spinning-disk confocal scanning fluorescence microscopy. This technique allows high-accuracy 3D refractive index (RI) profiling of live cells in spite of the missing projections. The cellular outer shape and its interior organelles measured by the confocal fluorescence imaging not only specify the cell in molecular levels, but also provide the 3D distributions of the whole cell as well as its organelles. We take these additional 3D morphological details as constraints in Gerchberg-Papoulis-based optical diffraction tomography algorithm. We then obtain an accurate 3D RI tomogram, even with a sparse angular range having a small number of perspective projections, otherwise providing low-accuracy RI reconstruction. Then, we obtain both cellular molecular specificity and inner RI values of the cell and its organelles. We compare the reconstructed 3D RI profiles of various samples, demonstrating the superiority of the proposed technique.     

Adaptive brightness fusion method for intraoperative near-infrared fluorescence and visible images

An adaptive brightness fusion method (ABFM) for near-infrared fluorescence imaging is proposed to adapt to different lighting conditions and make the equipment operation more convenient in clinical applications. The ABFM is designed based on the network structure of Attention Unet, which is an image segmentation technique. Experimental results show that ABFM has the function of adaptive brightness adjustment and has better fusion performance in terms of both perception and quantification. Generally, the proposed method can realize an adaptive brightness fusion of fluorescence and visible images to enhance the usability of fluorescence imaging technology during surgery.     

Dual-modality fluorescence and full-field optical coherence microscopy for biomedical imaging applications

Full-field optical coherence microscopy (FFOCM) is a high-resolution interferometric technique that is particularly attractive for biomedical imaging. Here we show that combining it with structured illumination fluorescence microscopy on one platform can increase its versatility since it enables co-localized registration of optically sectioned reflectance and fluorescence images. To demonstrate the potential of this dual modality, a fixed and labeled mouse retina was imaged. Results showed that both techniques can provide complementary information and therefore the system could potentially be useful for biomedical imaging applications.     

Deeply-supervised density regression for automatic cell counting in microscopy images

Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art density regression-based methods. First, the density regression model (DRM) is designed as a concatenated fully convolutional regression network (C-FCRN) to employ multi-scale image features for the estimation of cell density maps from given images. Second, auxiliary convolutional neural networks (AuxCNNs) are employed to assist in the training of intermediate layers of the designed C-FCRN to improve the DRM performance on unseen datasets. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.     

Low noise,self-phase-modulation-enabled femtosecond fiber sources tunable in 740-1236 nm for wide two-photon fluorescence microscopy applications

We have demonstrated widely tunable Yb:fiber-based laser sources, aiming to replace Ti:sapphire lasers for the nJ-level ultrafast applications, especially for the uses of nonlinear light microscopy. We investigated the influence of different input parameters to obtain an expansive spectral broadening, enabled by self-phase modulation and further reshaped by self-steepening, in the normal dispersion regime before the fiber damage. We also discussed the compressibility and intensity fluctuations of the demonstrated pulses, to reach the transform-limited duration with a very low intensity noise. Most importantly, we have demonstrated clear two-photon fluorescence images from UV-absorbing fluorophores to deep red dye stains.     

Fluorescence correlation spectroscopy and fluorescence lifetime imaging microscopy

With few and commercially available add-ons, both confocal and full-field fluorescence microscopes can be adapted to provide more information on the biological sample of interest. In this review we discuss the possibilities offered by two additional functionalities to fluorescence microscopes, fluorescence correlation spectroscopy (FCS) and fluorescence lifetime imaging mi croscopy (FLIM). FCS measurements at a single point in a sample allow kinetic and diffusion properties of fluorescently labeled molecules to be determined, as well as their concentration and aggregation state. Data from multiple points of the sample can be acquired using scanning-FCS, image correlation spectroscopy, and raster image correlation spectroscopy. These techniques cover phenomena with characteristic durations from sub-microsecond to second time scales. The power of FLIM lies in the fact that the measured fluorescent lifetime of a fluorophore is sensitive to the molecular environment of that fluorophore. FLIM is a robust means to quantify Forster resonance energy transfer and thus determine protein-protein interactions or protein conformational changes. In addition, FLIM is very valuable for functional imaging of ion concentrations in cells and tissues as it can be applied in heterogeneously labeled samples. In summary, FCS and FLIM allow information to be gathered beyond localization, including diffusional mobility, protein clustering and interactions, and molecular environment.