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Background Preservation and improvement of heart and vessel health is the primary motivation behind cardiovascular disease (CVD) research. Development of advanced imaging techniques can improve our understanding of disease physiology and serve as a monitor for disease progression. Various image processing approaches have been proposed to extract parameters of cardiac shape and function from different cardiac imaging modalities with an overall intention of providing full cardiac analysis. Due to differences in image modalities, the selection of an appropriate segmentation algorithm may be a challenging task. Purpose This paper presents a comprehensive and critical overview of research on the whole heart, bi-ventricles and left atrium segmentation methods from computed tomography (CT), magnetic resonance (MRI) and echocardiography (echo) imaging. The paper aims to: (1) summarize the considerable challenges of cardiac image segmentation, (2) provide the comparison of the segmentation methods, (3) classify significant contributions in the field and (4) critically review approaches in terms of their performance and accuracy. Conclusion The methods described are classified based on the used segmentation approach into (1) edge-based segmentation methods, (2) model-fitting segmentation methods and (3) machine and deep learning segmentation methods and are further split based on the targeted cardiac structure. Edge-based methods are mostly developed as semi-automatic and allow end-user interaction, which provides physicians with extra control over the final segmentation. Model-fitting methods are very robust and resistant to the high variability in image contrast and overall image quality. Nevertheless, they are often time-consuming and require appropriate models with prior knowledge. While the emerging deep learning segmentation approaches provide unprecedented performance in some specific scenarios and under the appropriate training, their performance highly depends on the data quality and the amount and the accuracy of provided annotations.

Specular reflections are undesirable phenomena that can impair overall perception and subsequent image analysis. In this paper, we propose a modern solution to this problem, based on the latest achievements in this field. The proposed method includes three main steps: image enhancement, detection of specular reflections, and reconstruction of damaged areas. To enhance and equalize the brightness characteristics of the image, we use the alpha-rooting method with an adaptive choice of the optimal parameter-alpha. To detect specular reflections, we apply morphological filtering in the HSV color space. At the final stage, there is a reconstruction of damaged areas using adversarial neural networks. This combination makes it possible to quickly and effectively detect and remove specular reflections, which is confirmed by a series of experiments given by the experimental section of this work.

In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.

In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects.

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects.

Background Percutaneous left atrial appendage (LAA) closure (placement of an occluder to close the appendage) is a novel procedure for stroke prevention in patients suffering from atrial fibrillation. The closure procedure planning requires accurate LAA measurements which can be obtained from computed tomography (CT) images. Method We propose a novel semi-automatic LAA segmentation method from 3D coronary CT angiography (CCTA) images. The method segments the LAA, proposes the location for the occluder placement (a delineation plane between the left atrium and LAA) and calculates measurements needed for closure procedure planning. The method requires only two inputs from the user: a threshold value and a single seed point inside the LAA. Proposed location of the delineation plane can be intuitively corrected if necessary. Measurements are calculated from the segmented LAA according to the final delineation plane. Results Performance of the proposed method is validated on 17 CCTA images, manually segmented by two medical doctors. We achieve the average dice coefficient overlap of 92.52% and 91.63% against the ground truth segmentations. The average dice coefficient overlap between the two ground truth segmentations is 92.66%. Our proposed LAA orifice localization is evaluated against the desired location of the LAA orifice determined by the expert. The average distance between our proposed location and the desired location is 2.51 mm. Conclusion Segmentation results show high correspondence to the ground truth segmentations. The occluder placement method shows high accuracy which indicates potential in clinical procedure planning.

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.

Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

In this Chapter, we review the basic principles of image restoration, and we discuss in more detail a new state-of-the art approach with applications in biomedical image processing. While the term image restoration is widely used in connection with the removal of various types of degradations, we address image restoration in the classical sense, which means estimating the underlying degradation-free image from the observations affected by noise and blur. This estimation process makes use of the degradation model of the image formation (for noise and blurring), which is often referred to as the data distribution. Next to this degradation model, the restoration process often makes use of a prior model, which encodes our prior knowledge about the image to be estimated, e.g., some of its statistical properties. The use of these models and clearly defined criteria for optimally estimating true values of the underlying degradation-free data, is what makes image restoration essentially different from image enhancement techniques (which are mainly heuristic and aim at improving the visual perception of the image only). In this Chapter, we address the Bayesian restoration framework, and we discuss the use of some popular statistical models, like multiresolution image representations. There we will illustrate the advantages of using geometry-adaptive representations with improved orientation selectivity (like complex wavelets, curvelets, shearlets and steerable pyramids) over the classical wavelets. Finally, we will present several applications in restoration of biomedical images obtained with a confocal microscope (an special kind of optical microscope that allows 3D imaging).

While many existing CT noise filtering post-processing techniques optimize minimum mean squared error (MSE)-based quality metrics, it is well-known that the MSE is generally not related to the diagnostic quality of CT images. In medical image quality assessment, model observers (MOs) have been proposed for predicting diagnostic quality in medical images. MOs optimize a task-based quality criterion such as lesion or tumor detection performance. In this paper, we first discuss some of the non-stationary noise properties of CT noise. These properties will be utilized to construct a multi-directional non-stationary noise model that can be used by MOs. Next, we investigate a new shearlet-based denoising scheme that opti- mizes a task-based image quality metric for CT background noise. This work makes a connection between multi-resolution sparsity-based denoising techniques on the one hand and model observers on the other hand. The main advantage is that this approach avoids the two-step procedure of MSE-optimized denoising followed by a MO-based quality evaluation (of- ten with contradictory quality goals), while instead optimizing the desired task-based image quality directly. Experimental results are given to illustrate the benefits of the proposed approach.

The problem of foreground detection in real-time video surveillance applications is addressed. Proposes is a framework, which is computationally cheap and has low memory requirements. It combines two simple processing blocks, both of which are essentially background subtraction algorithms. The main novelty of the approach is a combination of an autoregressive moving average filter with two background models having different adaptation speeds. The first model, having a lower adaptation speed, models long-term background and detects foreground objects by finding areas in the current frame which significantly differ from the proposed background model. The second model, with a higher adaptation speed, models the short-term background and is responsible for finding regions in the scene with a high foreground object activity. The final foreground detection is built by combining the outputs from these building blocks. The foreground obtained by the long-term modelling block is verified by the output of the short-term modelling block, i.e. only the objects exhibiting significant motion are detected as real foreground objects. The proposed method results in a very good foreground detection performance at a low computational cost.

Vehicle re-identification is key to keep track of vehicles monitored by a multicamera network with non-overlapping views. In this paper, we propose a probabilistic framework based on a two-step strategy that re-identifies vehicles in road tunnels. The first step consists of splitting the re-identification problem by connecting groups of vehicles observed in different cameras using certain motion and appearance criteria. In the second step, we build a Bayesian model that finds the optimal assignment between vehicles of connected groups. Descriptors like trace transform signatures, lane change, and motion discrepancies are used to derive our probabilistic framework. Experimental tests reveal that connected groups derived from the first step are composed of 4 vehicles on average. This allow us to constrain the number of candidate matches and increase the chances of getting the correct match. In the second step, our Bayesian model succeeds in matching vehicles among candidates with very similar appearance and under uneven illumination conditions. In general, our system reports a re-identification accuracy of 92% using a nearest-neighbor matcher, and 98% using a one-to-one matcher. These results outperform previous works and encourage us to further develop our solution for other re-identification applications.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI -i.e., Gibbs ringing (GR)-has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially. (C) 2015 Elsevier Inc. All rights reserved.

We present an analysis of weakly convex discontinuity adaptive (WCDA)models for regularizing three-dimensional (3D) quantitative microwave imaging. In particular, we are concerned with complex permittivity reconstructions from sparse measurements such that the reconstruction process is significantly accelerated. When dealing with such a highly underdetermined problem, it is crucial to employ regularization, relying in this case on prior knowledge about the structural properties of the underlying permittivity profile: we consider piecewise homogeneous objects. We present a numerical study on the choice of the potential function parameter for the Huber function and for two selected WCDA functions, one of which (the Leclerc-Cauchy-Lorentzian function) is designed to be more edge-preserving than the other (the Leclerc-Huber function). We evaluate the effect of reducing the number of (simulated) scattered field data on the reconstruction quality. Furthermore, reconstructions from subsampled single-frequency experimental data from the 3D Fresnel database illustrate the effectiveness of WCDA regularization.

Reconstructing complex permittivity profiles of dielectric objects from measurements of the microwave scattered field is a non-linear ill posed inverse problem. We analyze the performance of the Huber regularizer in the application, studying the influence of the parameters under different noise levels. Moreover, we evaluate the whole approach on real 3D electromagnetic measurements. Our focus is on reconstructions from relatively few measurements (sparse measurements) to speed up the reconstruction process.