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Titlebook: Information Technologies in Biomedicine; Volume 2 Ewa Pi?tka,Jacek Kawa Conference proceedings 2010 Springer Berlin Heidelberg 2010 Biomate

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發(fā)表于 2025-3-21 17:23:20 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Information Technologies in Biomedicine
副標題Volume 2
編輯Ewa Pi?tka,Jacek Kawa
視頻videohttp://file.papertrans.cn/466/465633/465633.mp4
概述Recent research on Information Technologies in Biomedicine.Results of the 2nd International Conference.Information Technologies in Biomedicine ITiB 2010 held at June 7 - 9, 2010 in Kamien Slaski, Pola
叢書名稱Advances in Intelligent and Soft Computing
圖書封面Titlebook: Information Technologies in Biomedicine; Volume 2 Ewa Pi?tka,Jacek Kawa Conference proceedings 2010 Springer Berlin Heidelberg 2010 Biomate
描述Information Technology in Biomedicine is an interdisciplinary research area, that bridges the gap between tethodological achievements in engineering and clinical requirements in medical diagnosis and therapy. In this book, members of the academic society of technical and medical background present their research results and clinical implementation in order to satisfy the functional requirements of authorized physicians for the benefit of the patients. An extended area is covered by the articles. It includes biomedical signals, medical image processing, computer-aided diagnosis and surgery, biometrics, healthcare and telemedicine, biomechanics, biomaterials, bioinformatics. Section on bronchoscopy presents the basis as well as new research studies performed in this field.Papers present various theoretical approaches and new methodologies based on fuzzy sets, mathematical statistics, mathematical morphology, fractals, wavelets, syntactic methods, artificial neural networks, graphs and many others.
出版日期Conference proceedings 2010
關鍵詞Biomaterial; Frames; Monitor; Osteotom; Racter; biomechanics; bone; classification; evolution; image analysis
版次1
doihttps://doi.org/10.1007/978-3-642-13105-9
isbn_softcover978-3-642-13104-2
isbn_ebook978-3-642-13105-9Series ISSN 1867-5662 Series E-ISSN 1867-5670
issn_series 1867-5662
copyrightSpringer Berlin Heidelberg 2010
The information of publication is updating

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Dynamics of the Clusterization Process in an Adaptative Method of Image Segmentationoming areas determining can be ommited. Homogeneous clusters (consisting of continuous groups of pixels) are the part of the growth process. The clusters are merged when the homogeneousness condition is fulfilled. The threshold value changes during segmentation process, appropriate to the changeable conditions.
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Symmetry Plane of the Brain on Perfusion MR Imagessis. Presented algorithm therefore searches for symmetry line taking into consideration only anatomical information on each slice, ignoring adjacent slices. After presenting the method and sample results, we present results of tests.
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An Instrument for Monitoring Inflow and Washout of an Optical Contrast Agent into the Braine reflectance at two wavelengths. Currently, the instrument has been modified in such a way that simultaneous measurements of diffuse reflectance and fluorescence can be carried out. We present results of measurement of diffuse reflectance and fluorescence during injection of an optical contrast agent in healthy volunteer.
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A Two-Step Method for Detection of Architectural Distortions in MammogramsD Fourier transform in polar coordinates (.). The method was tested with 33 mammograms containing ADs, from the database .. Experimental results are promising in comparison with the results of the model method and the efficiency of commercial CAD systems. The sensitivity of . amounts to 68% with 0.86 false positives per image.
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Segmentation of Stained Lymphoma Tissue Section Imagese between the traditional method results and the proposed method is small for images with up to 100 nuclei while in more complicated images with more then 100 nuclei and with nuclei clusters this difference increases.
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Complete Gradient Clustering Algorithm for?Features Analysis of X-Ray Imagesatures obtained by X-ray technique, have been analyzed. The proposed algorithm is expected to be an effective tool for recognizing wheat varieties. A comparison between the clustering results obtained from this method and the classical .-means clustering algorithm shows positive practical features of the Complete Gradient Clustering Algorithm.
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