Developable surfaces and minimal surfaces have been employed in many areas such as sheet-metal and plate-metal based industries, architecture, aviation and ship manufacture. However, nonplanar minimal surfaces cannot ...
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Three-dimensional (3D) ultrasound (US) is increasingly being introduced in the clinic, both for diagnostics and image guidance. Obtaining 3D volumes with 2D US probes is a two-step process. First, a positioning sensor...
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Three-dimensional (3D) ultrasound (US) is increasingly being introduced in the clinic, both for diagnostics and image guidance. Obtaining 3D volumes with 2D US probes is a two-step process. First, a positioning sensor must be attached to the probe;second, a reconstruction of a 3D volume can be performed into a regular voxel grid. Various algorithms have been used for performing 3D reconstruction based on 2D images. In this paper, we propose a new Hole-filling algorithm using Distance Weight interpolation, and we also apply it to generate the volume in our image-guided for surgical robot. First, the ultrasound frames and position information are compounded into a 3D volume using the Bin-filling method. Then, the Hole-filling method is used to repair gaps in the volume. We define the empty voxels by sorting the neighboring voxels into three parts, and averaging them to obtain the value to fill the empty voxels according to distance weighted. The empty voxel estimation can be improved by thresholding the range width of its neighboring voxels and adjusting it to the average values. The method is tested on a Hole-manipulated volume derived from a cropped 3D ultrasound volume of chicken kidney. Our method shows improved result compared to several tested existing methods, including voxel nearest neighbour(VNN) and spline function interpolation.
By analyzing the numerical characteristics of impulse noise images, in order to resolve spreading-noise problem resulted from dilating and eroding images base on mathematical morphology, an algorithm of dilating and e...
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Change detection on rasterized data is extremely dependent on accurate radiometric and geometric rectification. The development of processing tools able to minimise these requirements has been recognised since the lat...
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In this paper we discuss landmark based absolute localization of tiny autonomous mobile robots in a known environment. Landmark features are naturally occurring as it is not allowed to modify the environment with spec...
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This paper makes a study on content-based image retrieval algorithm for document image database. Given a query image the system returns overall similar images in database. For document images, we propose the algorithm...
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Automatic aorta segmentation and quantification in thoracic computed tomography (CT) images is important for detection and prevention of aortic diseases. This paper proposes an automatic aorta segmentation algorithm i...
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In this paper we present a new cross-platform approach for video game delivery in wired and wireless local networks. The developed 3D streaming and video streaming approaches enable users to access video games on set ...
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image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an app...
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ISBN:
(纸本)1901725340
image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.
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