J. Imaging, Volume 4, Issue 1 (January 2018) – 25 articles

A Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. In previous work we investigated which steps could improve the reconstruction performance for an in-vivo setting. In this work we improved this registration by proposing a stable region detection method as well as extracting matchable features based on a deep-learning approach. Finally, we extracted a measure for the image registration quality and the visibility condition. With experiments we show that the image registration performance is increased and more constant. Using these methods a system can be developed that supports the surgeon during the surgery, by giving feedback and providing a more complete overview of the placenta. Full article

Owing to their zero charge, neutrons are able to pass through thick layers of matter (typically several centimeters) while being sensitive to magnetic fields due to their intrinsic magnetic moment. Therefore, in addition to the conventional attenuation contrast image, the magnetic field inside and around a sample can be visualized by detecting changes of polarization in a transmitted beam. The method is based on the spatially resolved measurement of the cumulative precession angles of a collimated, polarized, monochromatic neutron beam that traverses a magnetic field or sample. Full article

Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections. Full article

Neutron Imaging is ideally suited for applications in cultural heritage even at small reactors with moderate image resolution. However, recently, high resolution imaging is being increasingly used for advanced studies, especially in paleontology. The special contrast for hydrogen and between neighboring elements in the periodic system allows for new applications that are not accessible for X-rays, like organic material in enclosed containers made of ceramics or metals, fossilized bones in chalk rock or in ferrous “red” beds, and even for animal and hominid teeth. Fission neutrons permit the examination of large samples that otherwise show large attenuation for thermal neutrons. Full article

Thermal imaging has been a mainstay of military applications and diagnostic engineering. However, there is currently no formalised procedure for the use of thermal imaging capable of standing up to judicial scrutiny. Using a scientifically sound characterisation method, we describe the cooling function of three common shoe types at an ambient room temperature of 22 °C (295 K) based on the digital output of a consumer-grade FLIR i50 thermal imager. Our method allows the reliable estimation of cooling time from pixel intensity values within a time interval of 3 to 25 min after shoes have been removed. We found a significant linear relationship between pixel intensity level and temperature. The calibration method allows the replicable determination of independent thermal cooling profiles for objects without the need for emissivity values associated with non-ideal black-body thermal radiation or system noise functions. The method has potential applications for law enforcement and forensic research, such as cross-validating statements about time spent by a person in a room. The use of thermal images can thus provide forensic scientists, law enforcement officials, and legislative bodies with an efficient and cost-effective tool for obtaining and interpreting time-based evidence. Full article

High dynamic range (HDR) imaging techniques are nowadays widely used in building research to capture luminances in the occupant field of view and investigate visual discomfort. This photographic technique also makes it possible to map sky luminances. Such images can be used for illuminating virtual scenes; the technique is called image-based lighting (IBL). This paper presents a work investigating IBL in a lighting quality research context for accelerating the development of appearance-driven performance indicators. Simulations were carried out using Radiance software. The ability of IBL to accurately predict indoor luminances is discussed by comparison with luminances from HDR photographs and luminances predicted by simulation in modeling the sky in several other more traditional ways. The present study confirms previous observations that IBL leads to similar luminance values than far less laborious simulations in which the sky is modeled based on outdoor illuminance measurements. IBL and these last methods minimize differences with HDR photographs in comparison to sky modeling not based on outdoor measurements. Full article

Multifunctional nanoparticles with superior imaging properties and therapeutic effects have been extensively developed for the nanomedicine. However, tumor-intrinsic barriers and tumor heterogeneity have resulted in low in vivo therapeutic efficacy. The poor in vivo targeting efficiency in passive and active targeting of nano-therapeutics along with the toxicity of nanoparticles has been a major problem in nanomedicine. Recently, image-guided nanomedicine, which can deliver nanoparticles locally using non-invasive imaging and interventional oncology techniques, has been paid attention as a new opportunity of nanomedicine. This short review will discuss the existing challenges in nanomedicine and describe the prospects for future image-guided nanomedicine. Full article

The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition. Full article

A chaos-based cryptosystem has been suggested and investigated since last decade because of its sensitivity to the initial condition, unpredictability and ergodicity properties. The paper introduces a new chaotic map which helps to enhance the security of image transmission by blending the superior fractal function with a new 2D-Sine Tent composite map (2D-STCM) to generate a key stream. A trajectory map of a proposed 2D-STCM show a wider chaotic range implies better unpredictability and ergodicity feature, suitable to design a cryptosystem. A fractal based image encryption increases the key space of the security key up-to hundreds of bits, thus secure the proposed cryptosystem from brute-force attack. The requirement of confusion and diffusion are fulfilled by applying chaotic circular pixel shuffling (CCPS) to change the pixel position repeatedly and the execution of an improved XOR operation i.e., complex XOR, designed to increase the encryption quality. The proposed cryptosystem has been analyzed using statistical analysis, key sensitivity, differential analysis and key space analysis. The experimental result proves that the new scheme has a high security level to protect the image transmission over the network. Full article

We introduce a tool to build a surface mesh able to deal with sparse, heterogeneous, non-parallel, cross-sectional, non-coincidental contours and show its application to reconstruct surfaces of the heart. In recent years, much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to better understand cardiovascular functions as well as predict different cardiac processes. As MRIs are becoming the standard for cardiac medical imaging, we tested our methodology on cardiac MRI data from standard acquisitions. However, the ability to accurately reconstruct heart anatomy in three dimensions commonly comes with fundamental challenges—notably, the trade-off between data fitting and expected visual appearance. Most current techniques can either require contours from parallel slices or, if multiple slice orientations are used, require an exact match between these contours. In addition, some methods introduce a bias by the use of prior shape models or by trade-offs between the data matching terms and the smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting, ensuring a good matching to the input data as well as pleasant interpolation characteristics. To assess our method in the task of cardiac mesh generations, we evaluated its performance on synthetic data obtained from a cardiac statistical shape model as well as on real data. Using a statistical shape model, we simulated standard cardiac MRI acquisitions planes and contour data. We performed a multi-parameter evaluation study using plausible cardiac shapes generated from the model. We also show that long axes contours as well as the most extremal slices (basal and apical) contain the most amount of structural information, and thus should be taken into account when generating anatomically relevant geometrical cardiovascular surfaces. Our method is both used on epicardial and endocardial left ventricle surfaces as well as on the right ventricle. Full article

This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region ( $FG{D}_{roi}$ ) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, the Support Vector Machine classifier is used to perform the classification, and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to $86.13\%$ and $82.02\%$ accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature. Full article

C-arm cone-beam computed tomography (CBCT) has been used recently to acquire images of the human knee joint under weight-bearing conditions to assess knee joint health under load. However, involuntary patient motion during image acquisition leads to severe motion artifacts in the subsequent reconstructions. The state-of-the-art uses fiducial markers placed on the patient’s knee to compensate for the induced motion artifacts. The placement of markers is time consuming, tedious, and requires user experience, to guarantee reliable motion estimates. To overcome these drawbacks, we recently investigated whether range imaging would allow to track, estimate, and compensate for patient motion using a range camera. We argue that the dense surface information observed by the camera could reveal more information than only a few surface points of the marker-based method. However, the integration of range-imaging with CBCT involves flexibility, such as where to position the camera and what algorithm to align the data with. In this work, three dimensional rigid body motion is estimated for synthetic data acquired with two different range camera trajectories: a static position on the ground and a dynamic position on the C-arm. Motion estimation is evaluated using two different types of point cloud registration algorithms: a pair wise Iterative Closest Point algorithm as well as a probabilistic group wise method. We compare the reconstruction results and the estimated motion signals with the ground truth and the current reference standard, a marker-based approach. To this end, we qualitatively and quantitatively assess image quality. The latter is evaluated using the Structural Similarity (SSIM). We achieved results comparable to the marker-based approach, which highlights the potential of both point set registration methods, for accurately recovering patient motion. The SSIM improved from 0.94 to 0.99 and 0.97 using the static and the dynamic camera trajectory, respectively. Accurate recovery of patient motion resulted in remarkable reduction in motion artifacts in the CBCT reconstructions, which is promising for future work with real data. Full article

In this paper, we investigate the usefulness of adding a box-constraint to the minimization of functionals consisting of a data-fidelity term and a total variation regularization term. In particular, we show that in certain applications an additional box-constraint does not effect the solution at all, i.e., the solution is the same whether a box-constraint is used or not. On the contrary, i.e., for applications where a box-constraint may have influence on the solution, we investigate how much it effects the quality of the restoration, especially when the regularization parameter, which weights the importance of the data term and the regularizer, is chosen suitable. In particular, for such applications, we consider the case of a squared $L^2$ data-fidelity term. For computing a minimizer of the respective box-constrained optimization problems a primal-dual semi-smooth Newton method is presented, which guarantees superlinear convergence. Full article

We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician’s assessment. Full article

Recent improvements in the field of assistive technologies have led to innovative solutions aiming at increasing the capabilities of people with disability, helping them in daily activities with applications that span from cognitive impairments to developmental disabilities. In particular, in the case of Autism Spectrum Disorder (ASD), the need to obtain active feedback in order to extract subsequently meaningful data becomes of fundamental importance. In this work, a study about the possibility of understanding the visual exploration in children with ASD is presented. In order to obtain an automatic evaluation, an algorithm for free (i.e., without constraints, nor using additional hardware, infrared (IR) light sources or other intrusive methods) gaze estimation is employed. Furthermore, no initial calibration is required. It allows the user to freely rotate the head in the field of view of the sensor, and it is insensitive to the presence of eyeglasses, hats or particular hairstyles. These relaxations of the constraints make this technique particularly suitable to be used in the critical context of autism, where the child is certainly not inclined to employ invasive devices, nor to collaborate during calibration procedures.The evaluation of children’s gaze trajectories through the proposed solution is presented for the purpose of an Early Start Denver Model (ESDM) program built on the child’s spontaneous interests and game choice delivered in a natural setting. Full article

The material lithium indium diselenide, a single crystal neutron sensitive semiconductor, has demonstrated its capabilities as a high resolution imaging device. The sensor was prepared with a 55 $\mathsf{\mu }$ m pitch array of gold contacts, designed to couple with the Timepix imaging ASIC. The resulting device was tested at the High Flux Isotope Reactor, demonstrating a response to cold neutrons when enriched in 95% ${}^6$ Li. The imaging system performed a series of experiments resulting in a <200 $\mathsf{\mu }$ m resolution limit with the Paul Scherrer Institute (PSI) Siemens star mask and a feature resolution of 34 $\mathsf{\mu }$ m with a knife-edge test. Furthermore, the system was able to resolve the University of Tennessee logo inscribed into a 3D printed 1 cm ${}^3$ plastic block. This technology marks the application of high resolution neutron imaging using a direct readout semiconductor. Full article

Background: Probabilistic neighborhood tractography aims to automatically segment brain white matter tracts from diffusion magnetic resonance imaging (dMRI) data in different individuals. It uses reference tracts as priors for the shape and length of the tract, and matching models that describe typical deviations from these. We evaluated new reference tracts and matching models derived from dMRI data acquired from 80 healthy volunteers, aged 25–64 years. Methods: The new reference tracts and models were tested in 50 healthy older people, aged 71.8 ± 0.4 years. The matching models were further assessed by sampling and visualizing synthetic tracts derived from them. Results: We found that data-generated reference tracts improved the success rate of automatic white matter tract segmentations. We observed an increased rate of visually acceptable tracts, and decreased variation in quantitative parameters when using this approach. Sampling from the matching models demonstrated their quality, independently of the testing data. Conclusions: We have improved the automatic segmentation of brain white matter tracts, and demonstrated that matching models can be successfully transferred to novel data. In many cases, this will bypass the need for training data and make the use of probabilistic neighborhood tractography in small testing datasets newly practicable. Full article

Current status of Bragg-edge/dip neutron transmission analysis/imaging methods is presented. The method can visualize real-space distributions of bulk crystallographic information in a crystalline material over a large area (~10 cm) with high spatial resolution (~100 μm). Furthermore, by using suitable spectrum analysis methods for wavelength-dependent neutron transmission data, quantitative visualization of the crystallographic information can be achieved. For example, crystallographic texture imaging, crystallite size imaging and crystalline phase imaging with texture/extinction corrections are carried out by the Rietveld-type (wide wavelength bandwidth) profile fitting analysis code, RITS (Rietveld Imaging of Transmission Spectra). By using the single Bragg-edge analysis mode of RITS, evaluations of crystal lattice plane spacing (d-spacing) relating to macro-strain and d-spacing distribution’s FWHM (full width at half maximum) relating to micro-strain can be achieved. Macro-strain tomography is performed by a new conceptual CT (computed tomography) image reconstruction algorithm, the tensor CT method. Crystalline grains and their orientations are visualized by a fast determination method of grain orientation for Bragg-dip neutron transmission spectrum. In this paper, these imaging examples with the spectrum analysis methods and the reliabilities evaluated by optical/electron microscope and X-ray/neutron diffraction, are presented. In addition, the status at compact accelerator driven pulsed neutron sources is also presented. Full article

Analytical based approaches in Optical Character Recognition (OCR) systems can endure a significant amount of segmentation errors, especially when dealing with cursive languages such as the Arabic language with frequent overlapping between characters. Holistic based approaches that consider whole words as single units were introduced as an effective approach to avoid such segmentation errors. Still the main challenge for these approaches is their computation complexity, especially when dealing with large vocabulary applications. In this paper, we introduce a computationally efficient, holistic Arabic OCR system. A lexicon reduction approach based on clustering similar shaped words is used to reduce recognition time. Using global word level Discrete Cosine Transform (DCT) based features in combination with local block based features, our proposed approach managed to generalize for new font sizes that were not included in the training data. Evaluation results for the approach using different test sets from modern and historical Arabic books are promising compared with state of art Arabic OCR systems. Full article

Understanding the liquid phases and solidification behaviors of multicomponent alloy systems becomes difficult as modern engineering alloys grow more complex, especially with the discovery of high-entropy alloys (HEAs) in 2004. Information about their liquid state behavior is scarce, and potentially quite complex due to the presence of perhaps five or more elements in equimolar ratios. These alloys are showing promise as high strength materials, many composed of solid-solution phases containing equiatomic CoCrCu, which itself does not form a ternary solid solution. Instead, this compound solidifies into highly phase separated regions, and the liquid phase separation that occurs in the alloy also leads to phase separation in systems in which Co, Cr, and Cu are present. The present study demonstrates that in-situ neutron imaging of the liquid phase separation in CoCrCu can be observed. The neutron imaging of the solidification process may resolve questions about phase separation that occurs in these alloys and those that contain Cu. These results show that neutron imaging can be utilized as a characterization technique for solidification research with the potential for imaging the liquid phases of more complex alloys, such as the HEAs which have very little published data about their liquid phases. This imaging technique could potentially allow for observation of immiscible liquid phases becoming miscible at specific temperatures, which cannot be observed with ex-situ analysis of solidified structures. Full article

The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease. Full article

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modeling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts. Full article

Cell migration is crucial in many processes of development and maintenance of multicellular organisms and it can also be related to disease, e.g., Cancer metastasis, when cells migrate to organs different to where they originate. A precise analysis of the cell shapes in biological studies could lead to insights about migration. However, in some cases, the interaction and overlap of cells can complicate the detection and interpretation of their shapes. This paper describes an algorithm to segment and analyse the shape of macrophages in fluorescent microscopy image sequences, and compares the segmentation of overlapping cells through different algorithms. A novel 2D matrix with multiscale angle variation, called the anglegram, based on the angles between points of the boundary of an object, is used for this purpose. The anglegram is used to find junctions of cells and applied in two different applications: (i) segmentation of overlapping cells and for non-overlapping cells; (ii) detection of the “corners” or pointy edges in the shapes. The functionalities of the anglegram were tested and validated with synthetic data and on fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. The information that can be extracted from the anglegram shows a good promise for shape determination and analysis, whether this involves overlapping or non-overlapping objects. Full article

We propose an integrated model of Block-Permutation-Based Encryption (BPBE) and Reversible Data Hiding (RDH). The BPBE scheme involves four processes for encryption, namely block scrambling, block-rotation/inversion, negative-positive transformation and the color component shuffling. A Histogram Shifting (HS) method is adopted for RDH in our model. The proposed scheme can be well suitable for the hierarchical access control system, where the data can be accessed with the different access rights. This scheme encrypts R, G and B components independently. Therefore, we can generate similar output images from different input images. Additionally, the key derivation scheme also provides the security according to the different access rights. Our scheme is also resilient against brute-force attacks and Jigsaw Puzzle Solvers (JPSs). Furthermore, the compression performance is also not severely degraded using a standard lossless compression method. Full article