Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.

Quantitation of lower urinary tract afferents in 3D reconstruction of lumbosacral spinal cord sections v1

This protocol details the 3D reconstruction of the lumbosacral spinal cord using alternating cryosections, and then goes through the steps required to quantify lower urinary tract afferents. Using TissueMaker (MBF Bioscience), images of alternating sections can be ordered and aligned prior to the production of a single image stack. In Neurolucida 360 (MBF Bioscience), regions of interest can be defined within the image stack, and the bouton-like immunolabelling of cholera toxin B can be segmented. Once saved, this data can then be extracted using Neurolucida Explorer (MBF Bioscience).

DCT based Fusion of Variable Exposure Images for HDRI

Combining images with different exposure settings are of prime importance in the field of computational photography. Both transform domain approach and filtering based approaches are possible for fusing multiple exposure images, to obtain the well-exposed image. We propose a Discrete Cosine Trans- form (DCT-based) approach for fusing multiple exposure images. The input image stack is processed in the transform domain by an averaging operation and the inverse transform is performed on the averaged image obtained to generate the fusion of multiple exposure image. The experimental observation leads us to the conjecture that the obtained DCT coefficients are indicators of parameters to measure well-exposedness, contrast and saturation as specified in the traditional exposure fusion based approach and the averaging performed indicates equal weights assigned to the DCT coefficients in this non- parametric and non pyramidal approach to fuse the multiple exposure stack.

Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex

It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevbarheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results.

Automatic segmentation of skin cells in multiphoton data using multi-stage merging

We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.

CalTrack: High Throughput Automated Calcium Transient Analysis in Cardiomyocytes

Rationale: Calcium transient analysis is central to understanding inherited and acquired cardiac physiology and disease. While the development of novel calcium reporters enables assays of CRISPR/Cas-9 genome edited pluripotent stem cell derived cardiomyocytes (iPSC-CMs) and primary adult cardiomyocytes, existing calcium-detection technologies are often proprietary and require specialist equipment, while open source workflows necessitate considerable user expertise and manual input. Objective: We aimed to develop an easy to use open source, adaptable, and automated analysis pipeline for measuring cellular calcium transients, from image stack to data output, inclusive of cellular identification, background subtraction, photobleaching correction, calcium transient averaging, calcium transient fitting, data collation and aberrant behavior recognition. Methods and Results: We developed CalTrack, a MatLab based algorithm, to monitor fluorescent calcium transients in living cardiomyocytes, including isolated single cells or those contained in 3-dimensional tissues or organoids and to analyze data acquired using photomultiplier tubes or employing line scans. CalTrack uses masks to segment cells allowing multiple cardiomyocyte transients to be measured from a single field of view. After automatically correcting for photobleaching, CalTrack averages and fits a string of transients and provides parameters that measure time to peak, time of decay, tau, F max /F 0 and others. We demonstrate the utility of CalTrack in primary and iPSC-derived cell lines in response to pharmacological compounds and in phenotyping cells carrying hypertrophic cardiomyopathy variants. Conclusions: CalTrack, an open source tool that runs on a local computer, provides automated high-throughput analysis of calcium transients in response to development, genetic or pharmacological manipulations, and pathological conditions. We expect that CalTrack analyses will accelerate insights into physiologic and abnormal calcium homeostasis that influence diverse aspects of cardiomyocyte biology.

Single-Shot Through-Focus Image Acquisition and Phase Retrieval From Chromatic Aberration and Multi-Angle Illumination

Phase recovery from a stack of through-focus intensity images is an effective non-interference quantitative phase imaging strategy. Nevertheless, the implementations of these methods are expensive and time-consuming because the distance between each through-focus plane has to be guaranteed by precision mechanical moving devices, and the multiple images must be acquired sequentially. In this article, we propose a single-shot through-focus intensity image stack acquisition strategy without any precision movement. Isolated LED units are used to illuminate the sample in different colors from different angles. Due to the chromatic aberration characteristics of the objective, the color-channel defocus images on the theoretical imaging plane are mutually laterally shifted. By calculating the shift amount of each sub-image area in each color channel, the distances between each through-focus image can be obtained, which is a critical parameter in transport of intensity equation (TIE) and alternating projection (AP). Lastly, AP is used to recover the phase distribution and realize the 3D localization of different defocus distances of the sample under test as an example. Both simulation and experiments are conducted to verify the feasibility of the proposed method.

Deep learning facilitated local deformation monitoring with large-scale SAR interferometry

<p>SAR interferometry has stepped in the big-data era, particularly with the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. Large amount of Sentinel-1 SAR images has been acquired and archived, allowing for generating thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications still focus on monitoring kilometer-scale local deformation, sparsely distributed in a large area. It is thus not effective to apply the time-series InSAR analysis to the whole image stack, but to focus on areas with deformation. Aiming at this target, we present our recent work built upon deep neural networks to firstly detect localized deformation and then carry on the time-series analysis on small interferogram patches with deformation signals.</p><p>Here, we first introduce our burst-based Sentinel-1 processor, which has been fully paralleled for large-scale InSAR processing. From these interferograms, we adapt and train several deep neural networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. We apply our networks for mining subsidence and landslides monitoring. Comparing with traditional time-series InSAR analysis, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and the propagation of possible unwrapping errors.</p><p>The presented methods introduce artificial intelligence to the time-series InSAR processing chain and make the mission of regularly monitoring localized deformation sparsely distributed in large scale feasible and more efficient. As future work, we can further improve the temporal resolution of InSAR based local deformation monitoring by training networks combining interferograms from C-band and L-band SAR images, which will be available soon from future SAR missions such as NiSAR and LuTan-1.</p>

A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis

Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.