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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.

Multichannel images, i.e., images of the same object or scene taken in different spectral bands or with different imaging modalities/settings, are common in many applications. For example, multispectral images contain several wavelength bands and hence, have richer information than color images. Multichannel magnetic resonance imaging and multichannel computed tomography images are common in medical imaging diagnostics, and multimodal images are also routinely used in art investigation. All the methods for grayscale images can be applied to multichannel images by processing each channel/band separately. However, it requires vast computational time, especially for the task of searching for overlapping patches similar to a given query patch. To address this problem, we propose a three-dimensional orthonormal tree-structured Haar transform (3D-OTSHT) targeting fast full search equivalent for three-dimensional block matching in multichannel images. The use of a three-dimensional integral image significantly saves time to obtain the 3D-OTSHT coefficients. We demonstrate superior performance of the proposed block matching.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to nonlocal image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighboring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text, and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident.

Breast tumor detection with microwaves is based on the difference in dielectric properties between normal and malignant tissues. The complex permittivity reconstruction of inhomogeneous dielectric biological tissues from microwave scattering is a nonlinear, ill-posed inverse problem. We proposed to use the Huber regularization in our previous work where some preliminary results for piecewise constant objects were shown. In this paper, we employ the Huber function as regularization in the even more challenging 3D piecewise continuous case of a realistic numerical breast phantom. The resulting reconstructions of complex permittivity profiles indicate potential for biomedical imaging.

The examination of abdominal aorta is an effective way to diagnose many cardiovascular diseases. Aortic stiffness measured by pulse wave velocity (PWV) calculation is a good estimate of overall cardiovascular health. Calculation of pulse wave velocity requires the length of abdominal aorta as an input parameter, while the structure of abdominal aorta can be used for diagnostic purposes. For the sake of non-invasive diagnostics, non-contrasted MRI images of aorta were used. Due to the "black-blood" imaging, a lot of artifacts are present and a robust centerline extraction method is needed. In this research we develop a novel graph-based method for extracting centerlines of abdominal aorta for length calculation. Our method is robust to artifacts and noise and applicable to any imaging modality.