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Image processing of 3D tomographic images to extract structural information of porous materials has become extremely important in porous media research with the commoditization of x-ray tomography equipment to the lab scale. Extracted pore networks from images using image analysis techniques enable transport properties calculation for bigger domains at a low computational cost, allowing pore-scale investigation of porous media over meaningful macroscopic length scales. The present study reports a pore network extraction algorithm to simultaneously extract void and solid networks from tomographic images of porous materials using simple image analysis techniques. Crucially, it includes connectivity and geometrical information of both void and solid phases as well as the interlinking of these phases with each other. Validation was obtained on networks extracted from simple cubic and random sphere packings over a range of porosities. The effective diffusivity in the void phase and thermal conductivity in the solid phase was then calculated and found to agree well with direct numerical simulation results on the images, as well as a range of experimental data. One important outcome of this work was a novel and accurate means of calculating interfacial areas between grains and voids directly from digital images, which is critical to many phenomena where phase interactions occur. The efficient ‘dual network’ algorithm is written in PYTHON using open source tools and provides a new way to study critical processes that depend on transport in both void and solid phase such as catalytic reactors and electrochemical systems.

Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced, (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%, (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach., Comment: 5 Pages, ICIP 2020

Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems. Database link: https://data.mendeley.com/datasets/prf8p9rwhd/draft?a=d9646d2f-5154-4d49-8eb3-e7989187a24c

Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores., SPIE Medical Imaging 2020 (Draft version)

An effective contrast enhancement method should not only improve the perceptual quality of an image but should also avoid adding any artifacts or affecting naturalness of images. This makes Contrast Enhancement Evaluation (CEE) a challenging task in the sense that both the improvement in image quality and unwanted side-effects need to be checked for. Currently, there is no single CEE metric that works well for all kinds of enhancement criteria. In this paper, we propose a new Multi-Criteria CEE (MCCEE) measure which combines different metrics effectively to give a single quality score. In order to fully exploit the potential of these metrics, we have further proposed to apply them on the decomposed image using wavelet transform. This new metric has been tested on two natural image contrast enhancement databases as well as on medical Computed Tomography (CT) images. The results show a substantial improvement as compared to the existing evaluation metrics. The code for the metric is available at: https://github.com/zakopz/MCCEE-Contrast-Enhancement-Metric

The performance of Lithium-Ion batteries (LIB’s) strongly depends on 3D microstructure and continued research is needed for the development and optimization of electrode designs to further reduce cost and improve performance and durability. In this work, a pore network modelling approach is presented to understand the structure-performance relationship of porous cathodes of LIB’s. It was demonstrated that pore network models can efficiently predict the rate-dependent capacity of an electrode using only a 3-phase tomogram as input. The developed modelling framework was used to perform structural analysis on two Li(Ni0.5Mn0.3Co0.2)O2 (NMC532) cathodes of different thickness and calendaring pressure and revealed important insights of microstructural heterogeneities inside porous structures, including spatial distribution of concentration, potential and state of lithiation in electrolyte, active material and carbon binder domain. The computational performance of the pore network model was analyzed, and excellent performance was demonstrated, taking hours instead of weeks for a similar direct numerical simulation. The novel modelling framework reported in this study will enable the study of local heterogeneities in other types of cathode material to help screen next-generation electrode designs, augmenting and informing time-consuming cell fabrication and laboratory testing.

Image-based modelling of porous media to study the transport and reaction processes has become an essential tool. The availability of increasingly large image datasets at high resolution creates a need to develop algorithms that can process massive size images at a low computational cost. This study presents an efficient workflow to extract pore networks from large size porous domains using a watershed segmentation with geometrical domain decomposition. The method subdivides a porous image into smaller overlapping subdomains and performs a watershed segmentation on each subdomain in parallel or serial modes of operation to save CPU time or memory RAM, respectively. The computational performance of the algorithm was analyzed on a large size image and found to consume 50 percent less memory and upto 7 times less CPU time than the standard watershed implementation. Pore networks of four massive digital rock images were extracted and the the effective permeability predicted by the networks agreed well with previously investigated values illustrating the accuracy of the method. An additional application of this method, taking advantage of the reduced computational cost, is the upgrading of low-resolution image. It was found that increasing the resolution of a coarse image leads to more accurate predictions by helping the watershed segmenation prouduce a more faithful pore network model. The developed algorithm is implemented in Python, and included in the open source project PoreSpy. It uses highly optimized and efficient modules such as Dask and Numba to obtain the maximum performance. The domain decomposition approach used here will also lend itself well to processing on distributed memory clusters, enabling the processing of even larger porous domains.

Pore-scale simulations of Li-ion battery electrodes were conducted using both pore-network modeling and direct numerical simulation. Ternary tomographic images of NMC811 cathodes were obtained and used to create the pore-scale computational domains. A novel network extraction method was developed to manage the extraction of N-phase networks which was used to extract all three phases of NMC-811 electrode along with their interconnections Pore network results compared favorably with direct numerical simulations (DNS) in terms of effective transport properties of each phase but were obtained in significantly less time. Simulations were then conducted with combined diffusion-reaction to simulate the limiting current behavior. It was found that when considering only ion and electron transport, the electrode structure could support current densities about 300 times higher than experimentally observed values. Additional case studies were conducted to illustrate the necessity of ternary images which allow separate consideration of carbon binder domain and active material. The results showed a 24.4% decrease in current density when the carbon binder was treated as a separate phase compared to lumping the CBD and active material into a single phase. The impact of nanoporosity in the carbon binder phase was also explored and found to enhance the reaction rate by 16.8% compared to solid binder. In addition, the developed technique used 58 times larger domain volume than DNS which opens up the possibility of modelling much larger tomographic data sets, enabling representative areas of typically inhomogeneous battery electrodes to be modelled accurately, and proposes a solution to the conflicting needs of high-resolution imaging and large volumes for image-based modelling. For the first time, three-phase pore network modelling of battery electrodes has been demonstrated and evaluated, opening the path towards a new modelling framework for lithium ion batteries. The described here was financially supported by the University of Engineering and Technology Lahore, Pakistan as well as the Natural Science and Engineering Research Council (NSERC) of Canada and in the UK by the Faraday Institution (EP/R042012/1 and EP/R042063/1). Pablo A. García-Salaberri thanks the support from the STFC Early Career Award (ST/R006873/1) during his stay at the Electrochemical Innovation Lab

The recent advances in 3D acquisition and display technologies have led to the use of stereoscopy for a wide range of applications. The quality assessment of such stereo data becomes of great interest especially when the reference image is not available. For this reason, we propose in this paper a no-reference 3D image quality assessment algorithm based on joint statistical modeling of the wavelet subband coefficients of the stereo pairs. More precisely, we resort to bivariate and multivariate statistical modeling of the texture images to build efficient statistical features. These features are then combined with the depth ones and used to predict the quality score based on machine learning tools. The proposed methods are evaluated on LIVE 3D database and the obtained results show the good performance of joint statistical modeling based approaches.

In the field of advance scientific research where world is moving towards less human resources and more machine. Distributed representation has been one of the major field in which the prospects of neural usage have shifted immensely. In this research paper we have discussed how distributed representations are being used in streaming data clustering, and how to build a sequential evolution model for a streaming news website. Firstly, the stream news data collected from the web are cut to several slices chronologically; in addition, word2vec models are built in different time periods for every slice of data; lastly, compare the relationship of any target word in sequential word2vec models and analyze the evolutional procedure of the target word. An experimental result shows that our method is able to emerge the trend of the relationship between target words which can't be easily measured through traditional methods.

International audience; In this paper, an adaptive method of cancellation of parasitic vibrations is presented for a Self-Mixing (SM) interferometric laser vibration sensor which has been coupled with a solid state accelerometer (SSA). Previously, this was achieved by using a pre-calibration of phase and gain mismatches over the complete bandwidth of the instrument. Such a pre-calibration is not only tedious to execute but also hinders a mass production of the instrument as every SSA-SM sensor couple requires customized calibration. On the other hand, the proposed method does not require any pre-calibration as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor. Two different adaptive algorithms, namely Recursive Least Squares (RLS) and Least Mean Squares (LMS) algorithms are tested and a comparison is established on the basis of parameter dependence, convergence time, computational cost and rms error. The proposed algorithms have provided improved results (mean error of 19.1nm and 20.2nm for LMS and RLS respectively) as compared to pre-calibration based results (mean error of 24.7 nm) for a laser wavelength of 785 nm. Simulated and experimental results thus demonstrate the utility of such an approach for embedded vibration sensing corrupted by extraneous parasitic motion. Index Terms-adaptive filter, self-mixing, vibration measurement, embedded sensing, optical feedback, laser instrument.

Virtual reality (VR) training simulators have started playing a vital role in enhancing surgical skills, such as hand-eye coordination in laparoscopy, and practicing surgical scenarios that cannot be easily created using physical models. We describe a new VR simulator for basic training in laparoscopy, i.e. SmartSIM, which has been developed using a generic open-source physics engine called the simulation open framework architecture (SOFA). This paper describes the systems perspective of SmartSIM including design details of both hardware and software components, while highlighting the critical design decisions. Some of the distinguishing features of SmartSIM include: (i) an easy-to-fabricate custom-built hardware interface; (ii) use of a generic physics engine to facilitate wider accessibility of our work and flexibility in terms of using various graphical modelling algorithms and their implementations; and (iii) an intelligent and smart evaluation mechanism that facilitates unsupervised and independent learning.

Medical image segmentation has gained greater attention over the past decade, especially in the field of image-guided surgery. Here, robust, accurate and fast segmentation tools are important for planning and navigation. In this work, we explore the Convolutional Neural Network (CNN) based approaches for multi-dataset segmentation from CT examinations. We hypothesize that selection of certain parameters in the network architecture design critically influence the segmentation results. We have employed two different CNN architectures, 3D-UNet and VGG-16, given that both networks are well accepted in the medical domain for segmentation tasks. In order to understand the efficiency of different parameter choices, we have adopted two different approaches. The first one combines different weight initialization schemes with different activation functions, whereas the second approach combines different weight initialization methods with a set of loss functions and optimizers. For evaluation, the 3D-UNet was trained with the Medical Segmentation Decathlon dataset and VGG-16 using LiTS data. The quality assessment done using eight quantitative metrics enhances the probability of using our proposed strategies for enhancing the segmentation results. Following a systematic approach in the evaluation of the results, we propose a few strategies that can be adopted for obtaining good segmentation results. Both of the architectures used in this work were selected on the basis of general acceptance in segmentation tasks for medical images based on their promising results compared to other state-of-the art networks. The highest Dice score obtained in 3D-UNet for the liver, pancreas and cardiac data was 0.897, 0.691 and 0.892. In the case of VGG-16, it was solely developed to work with liver data and delivered a Dice score of 0.921. From all the experiments conducted, we observed that two of the combinations with Xavier weight initialization (also known as Glorot), Adam optimiser, Cross Entropy loss (GloCEAdam) and LeCun weight initialization, cross entropy loss and Adam optimiser LecCEAdam worked best for most of the metrics in a 3D-UNet setting, while Xavier together with cross entropy loss and Tanh activation function (GloCEtanh) worked best for the VGG-16 network. Here, the parameter combinations are proposed on the basis of their contributions in obtaining optimal outcomes in segmentation evaluations. Moreover, we discuss that the preliminary evaluation results show that these parameters could later on be used for gaining more insights into model convergence and optimal solutions.The results from the quality assessment metrics and the statistical analysis validate our conclusions and we propose that the presented work can be used as a guide in choosing parameters for the best possible segmentation results for future works.