1,781 research outputs found

    Nonlinear optimisation method for image segmentation and noise reduction using geometrical intrinsic properties

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    This paper considers the optimisation of a nonlinear functional for image segmentation and noise reduction. Equations optimising this functional are derived and employed to detect edges using geometrical intrinsic properties such as metric and Riemann curvature tensor of a smooth differentiable surface approximating the original image. Images are then smoothed using a Helmholtz type partial differential equation. The proposed approach is shown to be very efficient and robust in the presence of noise, and the reported results demonstrate better performance than the conventional derivative based edge detectors

    A nonlinear variational method for signal segmentation and reconstruction using level set algorithm

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    A nonlinear functional is considered in this letter for segmentation and noise removal of piecewise continuous signals containing binary information contaminated with Gaussian noise. A discontinuity is defined as points in time scale that separates two signal segments with different amplitude spectra. Segmentation and noise removal of a piecewise continuous signal are obtained by deriving equations minimising the nonlinear functional. An algorithm based on the level set method is employed to implement the solutions minimising the functional. The proposed method is robust in noisy signals and can avoid local minima

    Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

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    Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates

    Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup

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    Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

    Patient-specific design of the right ventricle to pulmonary artery conduit via computational analysis

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    Cardiovascular prostheses are routinely used in surgical procedures to address congenital malformations, for example establishing a pathway from the right ventricle to the pulmonary arteries (RV-PA) in pulmonary atresia and truncus arteriosus. Currently available options are fixed size and have limited durability. Hence, multiple re-operations are required to match the patients’ growth and address structural deterioration of the conduit. Moreover, the pre-set shape of these implants increases the complexity of operation to accommodate patient specific anatomy. The goal of the research group is to address these limitations by 3D printing geometrically customised implants with growth capacity. In this study, patient-specific geometrical models of the heart were constructed by segmenting MRI data of patients using Mimics inPrint 2.0. Computational Fluid Dynamics (CFD) analysis was performed, using ANSYS CFX, to design customised geometries with better haemodynamic performance. CFD simulations showed that customisation of a replacement RV-PA conduit can improve its performance. For instance, mechanical energy dissipation and wall shear stress can be significantly reduced. Finite Element modelling also allowed prediction of the suitable thickness of a synthetic material to replicate the behaviour of pulmonary artery wall under arterial pressures. Hence, eliminating costly and time-consuming experiments based on trial-and-error. In conclusion, it is shown that patient-specific design is feasible, and these designs are likely to improve the flow dynamics of the RV-PA connection. Modelling also provides information for optimisation of biomaterial. In time, 3D printing a customised implant may simplify replacement procedures and potentially reduce the number of operations required over a life time, bringing substantial improvements in quality of life to the patient

    Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems

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    Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
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