In recent years, volumetric (3D) cardiac ultrasound imaging has become more readily available in daily clinical practice due to the introduction of matrix array transducer technology. To date, quantitative analysis of...
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ISBN:
(纸本)9781424443895;9781424443901
In recent years, volumetric (3D) cardiac ultrasound imaging has become more readily available in daily clinical practice due to the introduction of matrix array transducer technology. To date, quantitative analysis of these data sets typically requires a significant amount of user interaction. Recently, our teams introduced methods that could help in automating this process. On the one hand, an edge detectionalgorithm in combination with a deformable subdivision surface was presented for automatic segmentation of the LV cavity. A real-time, dynamic implementation of this segmentation approach in combination with a Kalman filter allows tracking the subendocardial boundary throughout the cardiac cycle. This method is referred to as RCTL. On the other hand, an automatic 3D motion estimation algorithm was presented in which subsequent image volumes are elastically registered using a B-spline transformation field. This method is called splineMIRIT. Both methods were applied to clinical data to extract relevant functional parameters on global left ventricular (LV) function (i.e. stroke volume (SV) and ejection fraction (EF)). Both methods show a good correlation with the reference method and might thus be used for fully automated estimation of global LV function. Given that RCTL is a fully integrated method (accounting for both segmentation and tracking) it seems to be the better approach towards extracting these parameters. However, whether this remains true when assessing parameters for regional LV function remains to be investigated.
Medical imaging has been extensively used for disease monitoring, treatment planning, diagnosis and computer aided surgery. Often, the acquired images are raw in nature, thus making them prone to being complex and noi...
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ISBN:
(纸本)9781479989973
Medical imaging has been extensively used for disease monitoring, treatment planning, diagnosis and computer aided surgery. Often, the acquired images are raw in nature, thus making them prone to being complex and noisy. A series of preprocessing and information extraction steps are therefore necessary in order for the relevant information to reach the medical practitioner. To this end, image denoising and edge detection play a vital role as a precursor to more advanced techniques in the medical image processing field. In this paper we have proposed an innovative mathematical morphology-based image denoising and edge detection method, for pre-processing of computed tomography (CT) images of the human heart. The morphological edge detectionalgorithm together with six different shaped structuring elements are implemented to preserve and detect the edges of the CT image while effectively suppressing noise, all at low computational cost. The experimental results affirm our approach's efficiency and capability in denoising and detecting salient edges from corrupted and complex medical images.
detection of bone area in X-ray images and methods of comparing such images on the basis on their content is still an issue which can be substantially improved. In this paper we present a new method of efficient bone ...
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ISBN:
(纸本)9781479975617
detection of bone area in X-ray images and methods of comparing such images on the basis on their content is still an issue which can be substantially improved. In this paper we present a new method of efficient bone identification and its description by a collection of simple geometrical shapes. The idea of this kind of bones description was to developed a method that could reduce the amount of data to minimum. The solution enables fast comparison of X-ray images by checking small amount of data. This kind of geometric description of bone area is designed to create a robust bone descriptor which will be used as image pattern for image comparison method. The assumption is to create a descriptor of X-ray digital image content, mining them in large databases and search and compare X-ray images on the basis of their content. The achievement of the objectives was possible through the use of an edge detection method modified by the authors. Application of our method of edge detection gives much more satisfactory results and possibilities to further process medical images than commonly used methods of edge detection.
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