Autoradiographic image analysis is a powerful and dominantly used method for the quantitative analysis of radioisotope distribution in small animals. Three-dimensional reconstruction of the brain from serial sections ...
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ISBN:
(纸本)0780376366
Autoradiographic image analysis is a powerful and dominantly used method for the quantitative analysis of radioisotope distribution in small animals. Three-dimensional reconstruction of the brain from serial sections of autoradiographic images allows volumetric visualization and analysis, coregistration with other 3D modalities, and anatomical standardization. We implemented an operating system and platform independent software in which the 3D reconstruction of the rat brain from the autoradiographic images using the image registration technique is possible. Image registration and a 3D reconstruction technique developed in this study would be useful for the quantitative analysis of the autoradiographic images.
A new method of monitoring spinal posture in the sagittal plane is presented. Posture is measured during activities of daily living utilizing accelerometer inclinations of six points along a subject's spine. The o...
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A new method of monitoring spinal posture in the sagittal plane is presented. Posture is measured during activities of daily living utilizing accelerometer inclinations of six points along a subject's spine. The over sampled, low frequency filtered data can be monitored and collected over extended periods (up to four weeks) utilizing the inexpensive, low-power, portable data logger developed in the course of this research. The data logger utilizes 7 processors to collect and store data onto an MS-DOS formatted CompactFlash card for later analysis. Two trials were done: regimented postures across a one hour period and unregimented activities during a five hour period. data was then visualized graphically and compared to noted postures and activities. The system was designed to be accurate to within /spl plusmn/0.5/spl deg/. Measured precision was found to be within /spl plusmn/0.39/spl deg/. This system will be useful for monitoring changes in back curvature resulting from surgery or treatment.
Effectiveness and clarity of software objects, their adherence to coding standards and programming habits of programmers are important features of overall quality of software systems. This paper proposes an approach t...
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Effectiveness and clarity of software objects, their adherence to coding standards and programming habits of programmers are important features of overall quality of software systems. This paper proposes an approach towards a quantitative software quality assessment with respect to extensibility, reusability, clarity and efficiency. It exploits techniques of Computational Intelligence (CI) that are treated as a consortium of granular computing, neural networks and evolutionary techniques. In particular, we take advantage of self-organizing maps to gain a better insight into the data, and study genetic decision trees-a novel algorithmic framework to carry out classification of software objects with respect to their quality. Genetic classifiers serve as a "quality filter" for software objects. Using these classifiers, a system manager can predict quality of software objects and identify low quality objects for review and possible revision. The approach is applied to an object-oriented visualization-based software system for biomedical dataanalysis.
Wavelet methods for geometry encoding is a recently emerged superset of multiresolution analysis which has proven to be very efficient in term of compression and adaptive transmission of 3D content. The decorrelating ...
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Wavelet methods for geometry encoding is a recently emerged superset of multiresolution analysis which has proven to be very efficient in term of compression and adaptive transmission of 3D content. The decorrelating power and space/scale localization of wavelets enable efficient compression of arbitrary meshes as well as progressive and local reconstruction. Recent techniques based on zerotree compression have shown to be among the best lossy mesh compression methods, while remaining compatible with selective transmission of geometric data at various level of detail. While some progressive reconstruction schemes have been proposed in the past, we show in this paper that this representation, recently proposed in the MPEG4 standard, can be efficiently used to perform real-time, view-dependent reconstruction of large meshes. The proposed system combines algorithms for local updates, cache management and server/client dialog. The local details management is an improvement of progressive reconstructions built on top of hierarchical structures. It enables fast, homogeneous accommodation and suppression of wavelet coefficients at any level of subdivision, with time complexity independent of the size of the reconstructed mesh. The cache structure wisely exploits the hierarchical character of the received data, in order to avoid redundant information transmission. The whole system enables the client to have total control on the quality of navigation according to its storage and processing capabilities, whatever the size of the mesh.
The isomap method has demonstrated promising results in finding low dimensional manifolds from samples in high dimensional input space. While conventional subspace methods compute L/sub 1/ or L/sub 2/ metrics to repre...
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The isomap method has demonstrated promising results in finding low dimensional manifolds from samples in high dimensional input space. While conventional subspace methods compute L/sub 1/ or L/sub 2/ metrics to represent distances between samples and apply principal component analysis or similar to induce linear manifolds, the isomap method estimates the geodesic distance between samples and then uses multidimensional scaling to induce a low dimensional manifold. Since the isomap method is based on the reconstruction principle, it may not be optimal from the classification viewpoint. We present an extended isomap method that utilizes the Fisher linear discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original isomap method for pattern classification. Furthermore, the extended isomap method shows promising results compared with the best classification methods in the literature.
Applies numerical image enhancement methods to THz time-domain spectroscopy images in order to improve the quality of experimental data suffering from low resolution and finite focus diameter aperture effects. As an e...
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Applies numerical image enhancement methods to THz time-domain spectroscopy images in order to improve the quality of experimental data suffering from low resolution and finite focus diameter aperture effects. As an example, the level of detail in a biomedical image of a histopathological sample is shown to increase significantly.
data mining has become a major academic research area over the last ten years. However, in the transition from academic prototypes to commercial products there have been few successes with commercial data mining appli...
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data mining has become a major academic research area over the last ten years. However, in the transition from academic prototypes to commercial products there have been few successes with commercial data mining applications failing to make any significant impact in the marketplace. In this paper it is argued that most data mining applications concentrate on the model-building phase of the data mining process and rarely engage the user in other stages. The paper reviews the challenges of producing successful data mining applications and in particular the role of information visualisation. Visualisation in data mining tends to be used to present final results, rather than playing an important part throughout the entire process. We argue that an immersive environment may provide the user with a more suitable interface than is commonly offered. We present a virtual data mining environment which attempts to integrate a data mining application interface and information visualisation in a seamless manner, using the concept of 'liquid data'.
We deal with the problem of analyzing and visualizing large-size volume data sets. To this aim, we consider multiresolution representations based on a decomposition of the field domain into tetrahedral cells. We compa...
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ISBN:
(纸本)0769515460
We deal with the problem of analyzing and visualizing large-size volume data sets. To this aim, we consider multiresolution representations based on a decomposition of the field domain into tetrahedral cells. We compare two types of multiresolution representations that differ on the rule applied to refine an initial coarse mesh: one is based on tetrahedron bisection, and one based on vertex split. The two representations can be viewed as instances of a common multiresolution model, that we call a multiresolution mesh. Encoding data structures for the two representations are briefly described. An experimental comparison on structured volume data sets is presented.
The Large Scale Digital Cell analysis System (LSDCAS) was designed to analyze large numbers of cells under a variety of experimental conditions. LSDCAS is an automated microscope system capable of monitoring of the or...
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The Large Scale Digital Cell analysis System (LSDCAS) was designed to analyze large numbers of cells under a variety of experimental conditions. LSDCAS is an automated microscope system capable of monitoring of the order of 1000 microscope fields over time intervals of up to one month. Although LSDCAS was originally designed to study stress-induced mitotic catastrophe, current efforts aim to provide imaging and data modeling/visualization technologies that will be useful in many areas of biological research. Several collaborative projects with University of Iowa researchers are underway involving the study of alterations in cell motility associated with metastasis; determination of the fate of cells which overproduce pro-oxidants, as well as the development of software to automatically determine the mode of cell death following exposure to cytotoxic agents. Ongoing extension of the capabilities of LSDCAS using image segmentation techniques based upon active appearance models will allow for the measurement of a variety of cellular endpoints. This report details our ongoing studies using LSDCAS; in particular its use in the quantitation of cell motility is described. Following these preliminary analyses, the report ends with a discussion of current development of LSDCAS to provide real-time fluorescence-based analysis of pro-oxidant production in cultured cells.
We extend the triangulation method of Lee et al. (1977) and present a tetrahedron method for the sequential mapping of points in high-dimensional space into three-space. The three-space preserves distances among nodes...
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ISBN:
(纸本)076951695X
We extend the triangulation method of Lee et al. (1977) and present a tetrahedron method for the sequential mapping of points in high-dimensional space into three-space. The three-space preserves distances among nodes of a tetrahedron. Whenever a new point is mapped, its distances to three points previously mapped are exactly preserved. Among these three points, one can be chosen as the point connected through a minimal spanning tree. Other two points can be chosen as the nearest neighbors among points previously mapped, or some fixed reference points. This method works well to display a moderate number of points where distances among points have to be maintained. It is also useful to display data clusters, and can be combined with linear mapping methods for visualizing large datasets. Various applications in pattern analysis are discussed.
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