Spatial based Expectation Maximizing (EM)

<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Computer-based estimation of circulating blood volume from ultrasound imagery

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. In recent years, ultrasound images of inferior vena cava (IVC) and internal jugular vein (IJV) have been used to assess volume status and guide fluid administration. This approach has limitations in that a skilled operator must perform repeated measurements over time. In this dissertation, we develop semi-automatic image processing algorithms for estimation and tracking of the IVC anterior-posterior (AP)-diameter and IJV crosssectional area in ultrasound videos. The proposed algorithms are based on active contours (ACs), where either the IVC AP-diameter or IJV CSA is estimated by minimization of an energy functional. More specifically, in chapter 2, we propose a novel energy functional based on the third centralized moment and show that it outperforms the functionals that are traditionally used with active contours (ACs). We combine the proposed functional with the polar contour representation and use it for segmentation of the IVC. In chapters 3 and 4, we propose active shape models based on ellipse; circle; and rectangles fitted inside the IVC as efficient, consistent and novel approaches to tracking and approximating the anterior-posterior (AP)-diameter even in the context of poor quality images. The proposed algorithms are based on a novel heuristic evolution functional that works very well with ultrasound images. In chapter 3, we show that the proposed active circle algorithm accurately, estimates the IVC AP-diameter. Although the estimated AP-diameter is very close to its actual value, the clinicians define the IVC AP-diameter as the largest vertical diameter of the IVC contour which deviates from its actual definition. To solve this problem and estimate the AP-diameter in the same way as its clinical definition, in chapter 4, we propose the active rectangle algorithm, where clinically measured AP-diameter is modeled as the height of a vertical thin rectangle. The results show that the AP-diameter estimated by the active rectangle algorithm is closer to its clinically measurement than the active circle and active ellipse algorithms. In chapter 5, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm has been applied to 65 ultrasound videos and shown to be a significant improvement over existing segmentation algorithms. So far, all proposed algorithms are semi-automatic as they need an operator to either locate the vessel in the first frame, or manually segment the first first and work automatically for the next frames. In chapter 6, we proposed a novel algorithm to automatically locate the vessel in ultrasound videos. The proposed algorithm is based on convolutional neural networks (CNNs) and is trained and applied for IJV videos. In this chapter we show that although the proposed algorithm is trained for data acquired from healthy subjects, it works efficiently for the data collected from coronary heart failure (CHF) patients without additional training. Finally, conclusions are drawn and possible extensions are discussed in chapter 7

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Neutro-Connectedness Theory, Algorithms and Applications

Connectedness is an important topological property and has been widely studied in digital topology. However, three main challenges exist in applying connectedness to solve real world problems: (1) the definitions of connectedness based on the classic and fuzzy logic cannot model the “hidden factors” that could influence our decision-making; (2) these definitions are too general to be applied to solve complex problem; and (4) many measurements of connectedness are heavily dependent on the shape (spatial distribution of vertices) of the graph and violate the intuitive idea of connectedness. This research focused on solving these challenges by redesigning the connectedness theory, developing fast algorithms for connectedness computation, and applying the newly proposed theory and algorithms to solve challenges in real problems. The newly proposed Neutro-Connectedness (NC) generalizes the conventional definitions of connectedness and can model uncertainty and describe the part and the whole relationship. By applying the dynamic programming strategy, a fast algorithm was proposed to calculate NC for general dataset. It is not just calculating NC map, and the output NC forest can discover a dataset’s topological structure regarding connectedness. In the first application, interactive image segmentation, two approaches were proposed to solve the two most difficult challenges: user interaction-dependence and intense interaction. The first approach, named NC-Cut, models global topologic property among image regions and reduces the dependence of segmentation performance on the appearance models generated by user interactions. It is less sensitive to the initial region of interest (ROI) than four state-of-the-art ROI-based methods. The second approach, named EISeg, provides user with visual clues to guide the interacting process based on NC. It reduces user interaction greatly by guiding user to where interacting can produce the best segmentation results. In the second application, NC was utilized to solve the challenge of weak boundary problem in breast ultrasound image segmentation. The approach can model the indeterminacy resulted from weak boundaries better than fuzzy connectedness, and achieved more accurate and robust result on our dataset with 131 breast tumor cases

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR