標(biāo)題: Titlebook: Handbook of Biomedical Imaging; Methodologies and Cl Nikos Paragios,James Duncan,Nicholas Ayache Book 2015 Springer Science+Business Media [打印本頁] 作者: Buchanan 時(shí)間: 2025-3-21 19:09
書目名稱Handbook of Biomedical Imaging影響因子(影響力)
書目名稱Handbook of Biomedical Imaging影響因子(影響力)學(xué)科排名
書目名稱Handbook of Biomedical Imaging網(wǎng)絡(luò)公開度
書目名稱Handbook of Biomedical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Biomedical Imaging被引頻次
書目名稱Handbook of Biomedical Imaging被引頻次學(xué)科排名
書目名稱Handbook of Biomedical Imaging年度引用
書目名稱Handbook of Biomedical Imaging年度引用學(xué)科排名
書目名稱Handbook of Biomedical Imaging讀者反饋
書目名稱Handbook of Biomedical Imaging讀者反饋學(xué)科排名
作者: OTHER 時(shí)間: 2025-3-21 23:25
http://image.papertrans.cn/h/image/420906.jpg作者: inflate 時(shí)間: 2025-3-22 03:38 作者: asthma 時(shí)間: 2025-3-22 04:39 作者: SKIFF 時(shí)間: 2025-3-22 09:10 作者: Spirometry 時(shí)間: 2025-3-22 13:18 作者: 尾隨 時(shí)間: 2025-3-22 17:41
,Deutsches Barock und franz?sische Klassik,This chapter discusses relationships between . approach to object delineation and other standard techniques optimizing segmentation boundaries. Graph cut method is presented in the context of globally optimal labeling of binary Markov Random Fields (MRFs). We review algorithms details and show several 2D and 3D examples.作者: 營養(yǎng) 時(shí)間: 2025-3-22 23:10
Object Segmentation and Markov Random FieldsThis chapter discusses relationships between . approach to object delineation and other standard techniques optimizing segmentation boundaries. Graph cut method is presented in the context of globally optimal labeling of binary Markov Random Fields (MRFs). We review algorithms details and show several 2D and 3D examples.作者: 辮子帶來幫助 時(shí)間: 2025-3-23 03:01 作者: THROB 時(shí)間: 2025-3-23 06:33
ion is done and how the most appropriate techniques are used to address demands and diagnoses. Radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors will find this to be an exceptional reference..978-1-4899-7775-5978-0-387-09749-7作者: 我不重要 時(shí)間: 2025-3-23 09:45 作者: 善于騙人 時(shí)間: 2025-3-23 17:17
https://doi.org/10.1007/978-3-531-91650-7apter, we provide an overview of the main components of this technique, illustrate recent efforts in its validation and sensitivity analysis and discuss preliminary clinical studies and future research directions.作者: ovation 時(shí)間: 2025-3-23 18:22
https://doi.org/10.1007/978-3-662-45366-7he scene and by the large variety of clinical applications. Maximization of mutual information of corresponding voxel intensities allows for fully automated registration of multimodality images without need for segmentation or user intervention, which makes it well suited for routine clinical use in作者: flaggy 時(shí)間: 2025-3-24 00:00
Statistical Computing on Non-Linear Spaces for Computational Anatomyolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings.作者: Allege 時(shí)間: 2025-3-24 02:56
Image-based haemodynamics simulation in intracranial aneurysmsapter, we provide an overview of the main components of this technique, illustrate recent efforts in its validation and sensitivity analysis and discuss preliminary clinical studies and future research directions.作者: conjunctivitis 時(shí)間: 2025-3-24 07:31 作者: 下級 時(shí)間: 2025-3-24 14:11
https://doi.org/10.1007/978-3-663-05122-0hematical framework for knowledge representation, information modeling at different levels, fusion of heterogeneous information, reasoning and decision making. In this chapter, we provide an overview of the potential of this theory in medical imaging, in particular for classification, segmentation a作者: 前兆 時(shí)間: 2025-3-24 17:03
,Helium — Grundlegende Eigenschaften,tutorial of level sets towards a flexible frame partition paradigm that could integrate edge-drive, regional-based and prior knowledge to object extraction. The central idea behind such an approach is to perform image partition through the propagation planar curves/surfaces. To this end, an objectiv作者: MAIM 時(shí)間: 2025-3-24 20:50
Zerlegung wasserstoffreicher Gasgemische,tation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed Tomography (CT) images based on Magnetic Resonance (MR) images.作者: Picks-Disease 時(shí)間: 2025-3-25 00:49 作者: irreparable 時(shí)間: 2025-3-25 03:54
https://doi.org/10.1007/978-3-662-58462-0 of a structure or set of structures across a population. They can be used to help interpret new images by finding the parameters which best match an instance of the model to the image. Two widely used methods for matching are the Active Shape Model and the Active Appearance Model. We describe the m作者: 疲勞 時(shí)間: 2025-3-25 08:39 作者: atopic-rhinitis 時(shí)間: 2025-3-25 13:04
Einleitung: Cultural Animal Studies,across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features us作者: Ischemic-Stroke 時(shí)間: 2025-3-25 19:22
Johannes Bilstein,Kristin Westphal coupled with medical images which play a crucial role in the diagnosis, planning, control and follow-up of therapy. In this paper, we discuss the issue of building patient-specific physical and physiological models from macroscopic observations extracted from medical images. We illustrate the topic作者: lactic 時(shí)間: 2025-3-25 22:01 作者: HAVOC 時(shí)間: 2025-3-26 02:41 作者: 生來 時(shí)間: 2025-3-26 04:59
Sandra Wesenberg,Frank Nestmanntures or morphological and morphometrical studies. Segmentation in medical imaging is however challenging because of problems linked to low contrast images, fuzzy object-contours, similar intensities with adjacent objects of interest, etc. Using . can help in the segmentation task. A widely used met作者: OMIT 時(shí)間: 2025-3-26 12:07 作者: burnish 時(shí)間: 2025-3-26 15:06
Schulische Inklusion und soziale Teilhabeer artifacts, pathologies,…) and geometric (bifurcations) non-linearities. Our method represents vessels/tubular structures as sequences of state vectors (vessel cuts/cross-sections), which are described by the position of the corresponding plane, the center of the vessel in this plane and its radiu作者: 槍支 時(shí)間: 2025-3-26 17:44
,Tierethik in der Tiergestützten Therapie,deformations have gained significant popularity in algorithms for the non-rigid registration of medical images. In this chapter we show how free-form deformations can be used in non-rigid registration to model complex local deformations of 3D organs. In particular, we discuss diffeomorphic and non-d作者: 拋物線 時(shí)間: 2025-3-26 23:45 作者: 預(yù)感 時(shí)間: 2025-3-27 03:41
Fuzzy methods in medical imaginghematical framework for knowledge representation, information modeling at different levels, fusion of heterogeneous information, reasoning and decision making. In this chapter, we provide an overview of the potential of this theory in medical imaging, in particular for classification, segmentation a作者: NAIVE 時(shí)間: 2025-3-27 05:35
Curve Propagation, Level Set Methods and Groupingtutorial of level sets towards a flexible frame partition paradigm that could integrate edge-drive, regional-based and prior knowledge to object extraction. The central idea behind such an approach is to perform image partition through the propagation planar curves/surfaces. To this end, an objectiv作者: 闡明 時(shí)間: 2025-3-27 10:59
Kernel Methods in Medical Imagingtation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed Tomography (CT) images based on Magnetic Resonance (MR) images.作者: 使成整體 時(shí)間: 2025-3-27 15:00 作者: Isolate 時(shí)間: 2025-3-27 19:14 作者: 補(bǔ)助 時(shí)間: 2025-3-28 01:18 作者: Ornament 時(shí)間: 2025-3-28 05:11 作者: GLIDE 時(shí)間: 2025-3-28 07:00 作者: Androgen 時(shí)間: 2025-3-28 11:36 作者: Legion 時(shí)間: 2025-3-28 16:02
Image-based haemodynamics simulation in intracranial aneurysmsnal Fluid Dynamics techniques to approximate the complex blood flow characteristics of healthy and diseased vessels. Advances in image quality, algorithmic sophistication and computing power are enabling the introduction of such technology not only as a biomedical research tool but also for clinical作者: 修改 時(shí)間: 2025-3-28 22:16
Atlas-based Segmentationtures or morphological and morphometrical studies. Segmentation in medical imaging is however challenging because of problems linked to low contrast images, fuzzy object-contours, similar intensities with adjacent objects of interest, etc. Using . can help in the segmentation task. A widely used met作者: 不能仁慈 時(shí)間: 2025-3-28 23:47
Integration of Topological Constraints in Medical Image Segmentationological “defects” or departures from the true topology of a structure due to segmentation errors can greatly reduce the utility of anatomical models. In this chapter we cover methods for integrating topological constraints into segmentation procedures in order to generate geometrically accurate and作者: 殖民地 時(shí)間: 2025-3-29 06:26 作者: 打擊 時(shí)間: 2025-3-29 10:21
Non-rigid registration using free-form deformationsdeformations have gained significant popularity in algorithms for the non-rigid registration of medical images. In this chapter we show how free-form deformations can be used in non-rigid registration to model complex local deformations of 3D organs. In particular, we discuss diffeomorphic and non-d作者: FLING 時(shí)間: 2025-3-29 14:08 作者: 不適 時(shí)間: 2025-3-29 19:15
Book 2015o-markers. It covers five crucial thematic areas: methodologies, statistical and physiological models, biomedical perception, clinical biomarkers, and emerging modalities and domains. .The dominant state-of-the-art methodologies for content extraction and interpretation of medical images include fuz作者: GET 時(shí)間: 2025-3-29 22:08
Fuzzy methods in medical imagingn making. In this chapter, we provide an overview of the potential of this theory in medical imaging, in particular for classification, segmentation and recognition of anatomical and pathological structures.作者: TOM 時(shí)間: 2025-3-30 03:19
Geometric Deformable Modelsen recent developments on topology, prior shape, intensity and motion, resolution, efficiency, robust optimization, and multiple objects are reviewed. Key equations and motivating and demonstrative examples are provided for many methods and guidelines for appropriate use are noted.作者: Spartan 時(shí)間: 2025-3-30 05:43
Active Shape and Appearance Modelsinstance of the model to the image. Two widely used methods for matching are the Active Shape Model and the Active Appearance Model. We describe the models and the matching algorithms, and give examples of their use.作者: 以煙熏消毒 時(shí)間: 2025-3-30 11:07
Building Patient-Specific Physical and Physiological Computational Models from Medical Imagesue of building patient-specific physical and physiological models from macroscopic observations extracted from medical images. We illustrate the topic of model personalization with concrete examples in brain shift modeling, hepatic surgery simulation, cardiac and tumor growth modeling. We conclude this article with scientific perspectives.作者: 熟練 時(shí)間: 2025-3-30 15:47 作者: 合并 時(shí)間: 2025-3-30 19:39
Integration of Topological Constraints in Medical Image Segmentation In this chapter we cover methods for integrating topological constraints into segmentation procedures in order to generate geometrically accurate and topologically correct models, which is critical for many clinical and research applications.作者: JAUNT 時(shí)間: 2025-3-30 23:50 作者: 怒目而視 時(shí)間: 2025-3-31 03:06
Sandra Wesenberg,Frank Nestmannanalysis and we propose our solution for atlas-based segmentation in MR image of the brain when large space-occupying lesions are present. Finally, we present the new research directions that aim at overcome current limitations of atlas-based segmentation approaches based on registration only.作者: 青石板 時(shí)間: 2025-3-31 06:25
Schulische Inklusion und soziale Teilhabea non-parametric way, the Particle Filter approach, that is able to express multiple hypotheses (branches). Validation using ground truth from clinical experts and very promising experimental results for the segmentation of the coronaries demonstrates the potential of the proposed approach.作者: 概觀 時(shí)間: 2025-3-31 10:43 作者: Extemporize 時(shí)間: 2025-3-31 15:57
