CONTEXT:Co-registration of ex-vivo histologic images with pre-operative imaging (e.g., magnetic resonance imaging [MRI]) can be used to align and map disease extent, and to identify quantitative imaging signatures. Ho...
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CONTEXT:Co-registration of ex-vivo histologic images with pre-operative imaging (e.g., magnetic resonance imaging [MRI]) can be used to align and map disease extent, and to identify quantitative imaging signatures. However, ex-vivo histology images are frequently sectioned into quarters prior to imaging.
AIMS:This work presents Histostitcher™, a software system designed to create a pseudo whole mount histology section (WMHS) from a stitching of four individual histology quadrant images.
MATERIALS AND METHODS:Histostitcher™ uses user-identified fiducials on the boundary of two quadrants to stitch such quadrants. An original prototype of Histostitcher™ was designed using the Matlab programming languages. However, clinical use was limited due to slow performance, computer memory constraints and an inefficient workflow. The latest version was created using the extensible imaging platform (XIP™) architecture in the C++ programming language. A fast, graphics processor unit renderer was designed to intelligently cache the visible parts of the histology quadrants and the workflow was significantly improved to allow modifying existing fiducials, fast transformations of the quadrants and saving/loading sessions.
RESULTS:The new stitching platform yielded significantly more efficient workflow and reconstruction than the previous prototype. It was tested on a traditional desktop computer, a Windows 8 Surface Pro table device and a 27 inch multi-touch display, with little performance difference between the different devices.
CONCLUSIONS:Histostitcher™ is a fast, efficient framework for reconstructing pseudo WMHS from individually imaged quadrants. The highly modular XIP™ framework was used to develop an intuitive interface and future work will entail mapping the disease extent from the pseudo WMHS onto pre-operative MRI.
We consider the problem of improving the accuracy and reliability of probabilistic white matter tractography methods by improving the built-in sampling scheme, which randomly draws, from a diffusion model such as the ...
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We consider the problem of improving the accuracy and reliability of probabilistic white matter tractography methods by improving the built-in sampling scheme, which randomly draws, from a diffusion model such as the orientation distribution function (ODF), a direction of propagation. Existing methods employing inverse transform sampling require an ad hoc thresholding step to prevent the less likely directions from being sampled. We herein propose to perform importance sampling of spherical harmonics, which redistributes an input point set on the sphere to match the ODF using hierarchical sample warping. This produces a point set that is more concentrated around the modes, allowing the subsequent inverse transform sampling to generate orientations that are in better accordance with the local fiber configuration. Integrated into a Kalman filter-based framework, our approach is evaluated through experiments on synthetic, phantom, and real datasets.
Wireless sensor networks (WSNs) have attracted a great deal of research due to their wide-range of potential applications. Sensor deployment and coverage problems are their important issues. This article briefly intro...
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This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining intensity information of pixels with spatial information a...
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This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood for each pixel. LPs correspond to a certain number of layers of local homogenous connected components. Using LPs, a feature vector (local primitive pattern, LPP) is constructed for each pixel. The feature vector includes information about the sizes and contrast differences of LPs within a disk as well as the repetitive frequency of LPs outside that disk. To test the efficiency of LPP, support vector machine (SVM) classification is utilized.
Sensor networks are increasingly used for tracking, monitoring, and observing spatial dynamic phenomena in the real world (e.g. urban area). In order to ensure an efficient deployment of a sensor network, several opti...
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Sensor networks are increasingly used for tracking, monitoring, and observing spatial dynamic phenomena in the real world (e.g. urban area). In order to ensure an efficient deployment of a sensor network, several optimization algorithms have been proposed in recent years. Most of these algorithms often rely on oversimplified sensor models. In addition, they do not consider information on the terrain topography, city models, and the presence of diverse obstacles in the sensing area (e.g. buildings, trees, poles). Only some of those optimization algorithms attempt to consider the terrain information in the optimization of a sensor network deployment. However, most of these algorithms consider that the spatial models used for this purpose are perfect representations of the reality and are not sensitive to the quality of the information. However, spatial models are simplified representations of a complex reality, and hence are inherently uncertain. In this paper we will investigate the impact of the spatial data quality on the optimization of a sensor network and its spatial coverage in an urban area. For this purpose, we will investigate specific implications of spatial data quality criteria for a 3D city model that will be used in sensor network optimization algorithms. Then, we will analyze the impact of some of those criteria on the estimation of sensor network coverage.!Afterwards, a case study for sensor network deployment in an urban area will be presented. This case study will demonstrate the impact of 3D city models quality on the estimation of coverage using global and local optimization algorithms. Finally, the results obtained from this experimentation will be presented and discussed.
In this paper, a palmprint identification and verification approach based on Pyramidal Histograms of Oriented Gradients (PHOG) and fast tree based matching is presented. In the feature extraction stage, proposed local...
In this paper, a palmprint identification and verification approach based on Pyramidal Histograms of Oriented Gradients (PHOG) and fast tree based matching is presented. In the feature extraction stage, proposed local histograms of oriented gradient are extracted in each level or scale of the Gaussian pyramid of the palmprint. This matter helps to extract high contrast and reliable lines. In the identification stage, features of the query palmprint are extracted and identified by matching against the training database palmprints or templates with tree based matching. The tree based matching makes verification and identification fast. The proposed method is evaluated by experiments on Hong Kong PolyU palmprint database. The experimental results show that the proposed method achieves a high performance according to identification and verification rate, over 99.85% accuracy, and high identification speed, which takes less than 2 seconds.
The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range...
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Here we present a modeling for electrical stimulation of retinal ganglion cell. This modeling can be used in epiretinal prosthesis to come up with proper electrical stimulation parameters. The model that we present is...
Here we present a modeling for electrical stimulation of retinal ganglion cell. This modeling can be used in epiretinal prosthesis to come up with proper electrical stimulation parameters. The model that we present is a novel one based on the anatomy of the cell. The excitation process of the electrically stimulated retinal cells is simulated in a two step procedure. In the first step the ganglion target cell is represented considering all of its compartments and in a second step the extracellular potential along the neural structure is calculated and finally the membrane voltage response to the applied stimulations is analyzed. A monopolar point electrode was positioned above the cell in an infinite and homogenous environment and the monophasic and biphasic pulses were used as stimulating current. The results show that the responses are in accordance with the values and diagrams of experimental results more than the previous models.
This paper presents the current status of Decapod's English font reconstruction. The Potrace algorithm and its parameters that affect glyph shape are examined. The visual fidelity of Decapod's font reconstruct...
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This paper presents the current status of Decapod's English font reconstruction. The Potrace algorithm and its parameters that affect glyph shape are examined. The visual fidelity of Decapod's font reconstruction is shown and compared to Adobe ClearScan TM . The font reconstruction details of the two methods are presented. The experiment demonstrates the capabilities of the two methods in reconstructing the font for a synthetic book typeset each time with one of six English fonts, three serif and three sans-serif. For both typefaces, Decapod is able to create a reusable TTF font that is embedded in the generated PDF document.
In outdoor video processing systems, the image frames of a video sequence are usually subjected to poor visibility and contrast in hazy or foggy weather conditions. A fast and efficient technique to improve the visibi...
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