Directional bilateral filters for smoothing fluorescence microscopy images

Images obtained through fluorescence microscopy at low numerical aperture (NA) are noisy and have poor resolution. Images of specimens such as F-actin filaments obtained using confocal or widefield fluorescence microscopes contain directional information and it is important that an image smoothing or filtering technique preserve the directionality. F-actin filaments are widely studied in pathology because the abnormalities in actin dynamics play a key role in diagnosis of cancer, cardiac diseases, vascular diseases, myofibrillar myopathies, neurological disorders, etc. We develop the directional bilateral filter as a means of filtering out the noise in the image without significantly altering the directionality of the F-actin filaments. The bilateral filter is anisotropic to start with, but we add an additional degree of anisotropy by employing an oriented domain kernel for smoothing. The orientation is locally adapted using a structure tensor and the parameters of the bilateral filter are optimized for within the framework of statistical risk minimization. We show that the directional bilateral filter has better denoising performance than the traditional Gaussian bilateral filter and other denoising techniques such as SURE-LET, non-local means, and guided image filtering at various noise levels in terms of peak signal-to-noise ratio (PSNR). We also show quantitative improvements in low NA images of F-actin filaments. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

Direct Measurement of Three-Dimensional Forces in Atomic Force Microscopy

Direct measurement of three-dimensional (3-D) forces between an atomic force microscope (AFM) probe and the sample benefits diverse applications of AFM, including force spectroscopy, nanometrology, and manipulation. This paper presents the design and evaluation of a measurement system, wherein the deflection of the AFM probe is obtained at two points to enable direct measurement of all the three components of 3-D tip-sample forces in real time. The optimal locations for measurement of deflection on the probe are derived for a conventional AFM probe. Further, a new optimal geometry is proposed for the probe that enables measurement of 3-D forces with identical sensitivity and nearly identical resolution along all three axes. Subsequently, the designed measurement system and the optimized AFM probe are both fabricated and evaluated. The evaluation demonstrates accurate measurement of tip-sample forces with minimal cross-sensitivities. Finally, the real-time measurement system is employed as part of a feedback control system to regulate the normal component of the interaction force, and to perform force-controlled scribing of a groove on the surface of polymethyl methacrylate.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

SeamSeg: Video Object Segmentation using Patch Seams

In this paper, we propose a technique for video object segmentation using patch seams across frames. Typically, seams, which are connected paths of low energy, are utilised for retargeting, where the primary aim is to reduce the image size while preserving the salient image contents. Here, we adapt the formulation of seams for temporal label propagation. The energy function associated with the proposed video seams provides temporal linking of patches across frames, to accurately segment the object. The proposed energy function takes into account the similarity of patches along the seam, temporal consistency of motion and spatial coherency of seams. Label propagation is achieved with high fidelity in the critical boundary regions, utilising the proposed patch seams. To achieve this without additional overheads, we curtail the error propagation by formulating boundary regions as rough-sets. The proposed approach out-perform state-of-the-art supervised and unsupervised algorithms, on benchmark datasets.

Synthesis and electron microscopy of high entropy alloy nanoparticles

This work provides a methodology for synthesizing isolated multi-component, high entropy alloy nanoparticles. Wet chemical synthesis technique was used to synthesis NiFeCrCuCo nanoparticles. As synthesized nanoparticles were spherical with an average size of 26.7 +/- 3.3 nm. Average composition of the as-synthesized nanoparticle dispersion was 26 +/- 2 at% Cr, 14 +/- 2 at% Fe, 10 +/- 0.6 at% Co, 25 +/- 0.1 at% Ni and 25 +/- 1.1 at% Cu. Compositional analysis of the nanoparticles conducted using the compositional line profile analysis and compositional mapping on a single nanoparticle level revealed a fairly uniform distribution of all the five component elements within the nanoparticle volume. Electron diffraction analysis clearly revealed that the structure of as-synthesized nanoparticles was face centered cubic. (C) 2015 Elsevier B.V. All rights reserved.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

Quantitative characterization of adhesion and stiffness of corneal lens of Drosophila melanogaster using atomic force microscopy

Atomic force Microscopy (AFM) has become a versatile tool in biology due to its advantage of high-resolution imaging of biological samples close to their native condition. Apart from imaging, AFM can also measure the local mechanical properties of the surfaces. In this study, we explore the possibility of using AFM to quantify the rough eye phenotype of Drosophila melanogaster through mechanical properties. We have measured adhesion force, stiffness and elastic modulus of the corneal lens using AFM. Various parameters affecting these measurements like cantilever stiffness and tip geometry are systematically studied and the measurement procedures are standardized. Results show that the mean adhesion force of the ommatidial surface varies from 36 nN to 16 nN based on the location. The mean stiffness is 483 +/- 5 N/m, and the elastic modulus is 3.4 +/- 0.05 GPa (95% confidence level) at the center of ommatidia. These properties are found to be different in corneal lens of eye expressing human mutant tau gene (mutant). The adhesion force, stiffness and elastic modulus are decreased in the mutant. We conclude that the measurement of surface and mechanical properties of D. melanogaster using AFM can be used for quantitative evaluation of `rough eye' surface. (C) 2015 Elsevier Ltd. All rights reserved.

Limited-view light sheet fluorescence microscopy for three dimensional volume imaging

We propose and demonstrate a limited-view light sheet microscopy (LV-LSM) for three dimensional (3D) volume imaging. Realizing that longer and frequent image acquisition results in significant photo-bleaching, we have taken limited angular views (18 views) of the macroscopic specimen and integrated with maximum likelihood (ML) technique for reconstructing high quality 3D volume images. Existing variants of light-sheet microscopy require both rotation and translation with a total of approximately 10-fold more views to render a 3D volume image. Comparatively, LV-LSM technique reduces data acquisition time and consequently minimizes light-exposure by many-folds. Since ML is a post-processing technique and highly parallelizable, this does not cost precious imaging time. Results show noise-free and high contrast volume images when compared to the state-of-the-art selective plane illumination microscopy. (C) 2015 AIP Publishing LLC.

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

Outstanding dielectric constant and piezoelectric coefficient in electrospun nanofiber mats of PVDF containing silver decorated multiwall carbon nanotubes: assessing through piezoresponse force microscopy

In order to enhance the piezoelectric b-phase, PVDF was electrospun from DMF solution. The enhanced b-phase was discerned by comparing the electrospun fibers against the melt mixed samples. While both the processes resulted in phase transformation of a-to electroactive b-polymorph in PVDF, the fraction of b-phase was strongly dependent on the adopted process. Two different nanoscopic particles: carboxyl functionalized multiwall carbon nanotubes (CNTs) and silver (Ag) decorated CNTs were used to further enhance the piezoelectric coefficient in the electrospun fibers. Fourier transform infrared spectroscopy (FTIR) and wide-angle X-ray diffraction (XRD) supports the development of piezoelectric b-phase in PVDF. It was concluded that electrospinning was the best technique for inducing the b-polymorph in PVDF. This was attributed to the high voltage electrostatic field that generates extensional forces on the polymer chains that aligns the dipoles in one direction. The ferroelectric and piezoelectric measurement on electrospun fibers were studied using piezo-response force microscope (PFM). The Ag-CNTs filled PVDF electrospun fibers showed the highest piezoelectric coefficient (d(33) = 54 pm V-1) in contrast to PVDF/CNT fibers (35 pm V-1) and neat PVDF (30 pm V-1). This study demonstrates that the piezoelectric coefficient can be enhanced significantly by electrospinning PVDF containing Ag decorated nanoparticles.

Clean localization super-resolution microscopy for 3D biological imaging

We propose clean localization microscopy (a variant of fPALM) using a molecule filtering technique. Localization imaging involves acquiring a large number of images containing single molecule signatures followed by one-to-one mapping to render a super-resolution image. In principle, this process can be repeated for other z-planes to construct a 3D image. But, single molecules observed from off-focal planes result in false representation of their presence in the focal plane, resulting in incorrect quantification and analysis. We overcome this with a single molecule filtering technique that imposes constraints on the diffraction limited spot size of single molecules in the image plane. Calibration with sub-diffraction size beads puts a natural cutoff on the actual diffraction-limited size of single molecules in the focal plane. This helps in distinguishing beads present in the focal plane from those in the off-focal planes thereby providing an estimate of the single molecules in the focal plane. We study the distribution of actin (labeled with a photoactivatable CAGE 552 dye) in NIH 3T3 mouse fibroblast cells. (C) 2016 Author(s).

High-throughput miniaturized microfluidic microscopy with radially parallelized channel geometry

In this article, we present a novel approach to throughput enhancement in miniaturized microfluidic microscopy systems. Using the presented approach, we demonstrate an inexpensive yet high-throughput analytical instrument. Using the high-throughput analytical instrument, we have been able to achieve about 125,880 cells per minute (more than one hundred and twenty five thousand cells per minute), even while employing cost-effective low frame rate cameras (120 fps). The throughput achieved here is a notable progression in the field of diagnostics as it enables rapid quantitative testing and analysis. We demonstrate the applicability of the instrument to point-of-care diagnostics, by performing blood cell counting. We report a comparative analysis between the counts (in cells per mu l) obtained from our instrument, with that of a commercially available hematology analyzer.

Framework for morphometric classification of cells in imaging flow cytometry

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis. Lay description In this article, we propose a novel framework for processing the raw data generated using microfluidics based imaging flow cytometers. Microfluidics microscopy or microfluidics based imaging flow cytometry (mIFC) is a recent microscopy paradigm, that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy, which allows us imaging cells while they are in flow. In comparison to the conventional slide-based imaging systems, mIFC is a nascent technology enabling high throughput imaging of cells and is yet to take the form of a clinical diagnostic tool. The proposed framework process the raw data generated by the mIFC systems. The framework incorporates several steps: beginning from pre-processing of the raw video frames to enhance the contents of the cell, localising the cell by a novel, fully automatic, non-iterative graph based algorithm, extraction of different quantitative morphological parameters and subsequent classification of cells. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using cost-effective microfluidics based imaging flow cytometer. The cell lines of HL60, K562 and MOLT were obtained from ATCC (American Type Culture Collection) and are separately cultured in the lab. Thus, each culture contains cells from its own category alone and thereby provides the ground truth. Each cell is localised by finding a closed cell contour by defining a directed, weighted graph from the Canny edge images of the cell such that the closed contour lies along the shortest weighted path surrounding the centroid of the cell from a starting point on a good curve segment to an immediate endpoint. Once the cell is localised, morphological features reflecting size, shape and complexity of the cells are extracted and used to develop a support vector machine based classification system. We could classify the cell-lines with good accuracy and the results were quite consistent across different cross validation experiments. We hope that imaging flow cytometers equipped with the proposed framework for image processing would enable cost-effective, automated and reliable disease screening in over-loaded facilities, which cannot afford to hire skilled personnel in large numbers. Such platforms would potentially facilitate screening camps in low income group countries; thereby transforming the current health care paradigms by enabling rapid, automated diagnosis for diseases like cancer.