標(biāo)題: Titlebook: Computational Intelligence in Biomedical Imaging; Kenji Suzuki Book 2014 Springer Science+Business Media New York 2014 artificial neural n [打印本頁] 作者: 果園 時(shí)間: 2025-3-21 18:48
書目名稱Computational Intelligence in Biomedical Imaging影響因子(影響力)
書目名稱Computational Intelligence in Biomedical Imaging影響因子(影響力)學(xué)科排名
書目名稱Computational Intelligence in Biomedical Imaging網(wǎng)絡(luò)公開度
書目名稱Computational Intelligence in Biomedical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Intelligence in Biomedical Imaging被引頻次
書目名稱Computational Intelligence in Biomedical Imaging被引頻次學(xué)科排名
書目名稱Computational Intelligence in Biomedical Imaging年度引用
書目名稱Computational Intelligence in Biomedical Imaging年度引用學(xué)科排名
書目名稱Computational Intelligence in Biomedical Imaging讀者反饋
書目名稱Computational Intelligence in Biomedical Imaging讀者反饋學(xué)科排名
作者: Ordnance 時(shí)間: 2025-3-21 23:50 作者: Wordlist 時(shí)間: 2025-3-22 02:01
A Novel Image-Based Approach for Early Detection of Prostate Cancer Using DCE-MRIhe proposed approach consists of four main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a maximum a posteriori (MAP) estimate of a log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance o作者: 隱士 時(shí)間: 2025-3-22 05:55
Computational Intelligent Image Analysis for Assisting Radiation Oncologists’ Decision Making in Radprecision radiation therapy. The radiation therapy consists of five steps, i.e., diagnosis, treatment planning, patient setup, treatment, and follow-up, in which computational intelligent image analysis and pattern recognition methods play important roles in improving the accuracy of radiation thera作者: DNR215 時(shí)間: 2025-3-22 11:31 作者: FEAS 時(shí)間: 2025-3-22 13:05
Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmsteps. First, an anisotropic diffusion smoothing filter was applied to T1-weighted MR images of the liver in the portal-venous phase to reduce noise while preserving the liver boundaries. An edge enhancer and a nonlinear gray-scale converter were applied to enhance the liver boundary. This boundary-作者: FEAS 時(shí)間: 2025-3-22 17:51
Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Imagese on normal vertebral anatomy is essential to understand the vertebral fracture risk. Multi-detector row computed tomography (MDCT) method can be used for quantitative analysis of vertebral anatomy such as volumetric bone mineral density (vBMD), geometry, and alignment with high accuracy and precisi作者: 與野獸博斗者 時(shí)間: 2025-3-23 00:55
Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimizationissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to its higher attenuation, inhomogeneous appearance, and inconsistent texture. We overcome these challenges through a multi-layer machine learning approach that exploits geometric structures 作者: 打谷工具 時(shí)間: 2025-3-23 04:59 作者: Carcinogen 時(shí)間: 2025-3-23 08:12
Image Segmentation for Connectomics Using Machine Learningor neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit a作者: Coronary-Spasm 時(shí)間: 2025-3-23 12:38
Image Analysis Techniques for the Quantification of Brain Tumors on MR Images Although functional imaging provides rich information for diagnosis and treatment planning, practical considerations such as cost and availability currently limit its clinical utility. As a result, structural imaging methods that provide detailed information about the anatomical structures of the b作者: Acetabulum 時(shí)間: 2025-3-23 16:28
Respiratory and Cardiac Function Analysis on the Basis of Dynamic Chest Radiography for quantifying and visualizing cardiopulmonary function on dynamic chest radiographs. The measurement parameters are diaphragm motion, heart wall motion, pulmonary ventilation, and blood circulation. We will first introduce evaluation items, physiology, and diagnostic findings and then describe im作者: 范例 時(shí)間: 2025-3-23 20:59 作者: labyrinth 時(shí)間: 2025-3-23 23:43 作者: patriot 時(shí)間: 2025-3-24 02:24 作者: DRAFT 時(shí)間: 2025-3-24 07:56
Strategic Choice and SHRM and ERomputer-aided treatment planning methods including automated beam arrangement based on similar cases, computerized contouring of lung tumor regions using a support vector machine (SVM) classifier, and a computerized method for determination of robust beam directions against patient setup errors in p作者: 類型 時(shí)間: 2025-3-24 13:58 作者: photophobia 時(shí)間: 2025-3-24 18:55
SHRM & ER: The Resource-Based Viewerized scheme to quantify the vertebral geometry. The scheme provided appropriate values on the vertebral geometry with numerous CT cases. It is likely that such computer-based attempts will help us to achieve the sophisticated vertebral anatomy.作者: 光亮 時(shí)間: 2025-3-24 19:42 作者: GLOOM 時(shí)間: 2025-3-25 01:27 作者: Ledger 時(shí)間: 2025-3-25 03:22
Individual Productivity: A Sourcing Analysisnifest with gross anatomical changes that are visually similar, which limits the use of MRI in differentiating between them. Computer-aided image analysis enables a quantitative description of brain anatomy and detection of subtle, but important, anatomical changes that may be difficult to detect by作者: chance 時(shí)間: 2025-3-25 10:23 作者: MONY 時(shí)間: 2025-3-25 14:20
A Novel Image-Based Approach for Early Detection of Prostate Cancer Using DCE-MRIence of the patient. In the final step, we collect two features from these curves and use a .-nearest neighbor (KNN) classifier to distinguish between malignant and benign detected tumors. Moreover, in this chapter we introduce a new approach to generate color maps that illustrate the propagation of作者: V洗浴 時(shí)間: 2025-3-25 15:53 作者: 厭惡 時(shí)間: 2025-3-25 20:52
Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomogratraint. The liver, spleen, left kidney, right kidney and pancreas are concomitantly analyzed in the multi-organ analysis framework. Finally, the automated detection and segmentation of abdominal tumors (i.e., hepatic tumors) from abdominal CT images is presented using once again shape and enhancemen作者: 羊欄 時(shí)間: 2025-3-26 02:55
Computer-Aided Image Analysis for Vertebral Anatomy on X-Ray CT Imageserized scheme to quantify the vertebral geometry. The scheme provided appropriate values on the vertebral geometry with numerous CT cases. It is likely that such computer-based attempts will help us to achieve the sophisticated vertebral anatomy.作者: 客觀 時(shí)間: 2025-3-26 05:56
Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANnsity of bones are different from location to location and the capability of a single set of multi-resolution MTANNs is limited. To address this issue, the anatomically specific multiple MTANNs developed in this work were designed to separate bones from soft tissue in different anatomic segments of 作者: 蝕刻術(shù) 時(shí)間: 2025-3-26 12:08
Image Segmentation for Connectomics Using Machine Learningudying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.作者: 乳白光 時(shí)間: 2025-3-26 14:30
Image Analysis Techniques for the Quantification of Brain Tumors on MR Imagesnifest with gross anatomical changes that are visually similar, which limits the use of MRI in differentiating between them. Computer-aided image analysis enables a quantitative description of brain anatomy and detection of subtle, but important, anatomical changes that may be difficult to detect by作者: arrhythmic 時(shí)間: 2025-3-26 18:39 作者: llibretto 時(shí)間: 2025-3-26 23:03 作者: gerrymander 時(shí)間: 2025-3-27 02:44
Book 2014ave reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.作者: 法律的瑕疵 時(shí)間: 2025-3-27 06:29 作者: Pruritus 時(shí)間: 2025-3-27 11:20 作者: Fallibility 時(shí)間: 2025-3-27 17:15
Strategic Learning and Developmenthe proposed approach consists of four main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a maximum a posteriori (MAP) estimate of a log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance o作者: dysphagia 時(shí)間: 2025-3-27 18:50
Strategic Choice and SHRM and ERprecision radiation therapy. The radiation therapy consists of five steps, i.e., diagnosis, treatment planning, patient setup, treatment, and follow-up, in which computational intelligent image analysis and pattern recognition methods play important roles in improving the accuracy of radiation thera作者: 樂意 時(shí)間: 2025-3-27 22:47
Strategic Learning and Developmentsis also relies on the comprehensive analysis of multiple organs and quantitative measures of tissue. This chapter highlights our recent contributions to abdominal multi-organ analysis employing constraints typical to medical images and adapted to patient data. A new formulation for graph-based meth作者: 步兵 時(shí)間: 2025-3-28 04:33
SHRM & ER: The Resource-Based Viewsteps. First, an anisotropic diffusion smoothing filter was applied to T1-weighted MR images of the liver in the portal-venous phase to reduce noise while preserving the liver boundaries. An edge enhancer and a nonlinear gray-scale converter were applied to enhance the liver boundary. This boundary-作者: Felicitous 時(shí)間: 2025-3-28 07:46
SHRM & ER: The Resource-Based Viewe on normal vertebral anatomy is essential to understand the vertebral fracture risk. Multi-detector row computed tomography (MDCT) method can be used for quantitative analysis of vertebral anatomy such as volumetric bone mineral density (vBMD), geometry, and alignment with high accuracy and precisi作者: 愛社交 時(shí)間: 2025-3-28 13:28
Case 2: Work-Life Balance in an MNE Contextissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to its higher attenuation, inhomogeneous appearance, and inconsistent texture. We overcome these challenges through a multi-layer machine learning approach that exploits geometric structures 作者: 貿(mào)易 時(shí)間: 2025-3-28 18:22 作者: 符合規(guī)定 時(shí)間: 2025-3-28 19:25
Individual Productivity: A Sourcing Analysisor neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit a作者: BLAND 時(shí)間: 2025-3-29 02:00
Individual Productivity: A Sourcing Analysis Although functional imaging provides rich information for diagnosis and treatment planning, practical considerations such as cost and availability currently limit its clinical utility. As a result, structural imaging methods that provide detailed information about the anatomical structures of the b作者: Picks-Disease 時(shí)間: 2025-3-29 03:13 作者: Working-Memory 時(shí)間: 2025-3-29 08:27 作者: 模仿 時(shí)間: 2025-3-29 14:06
https://doi.org/10.1007/978-1-4613-1875-0iques based on a 3D morphological filtering technique and a temporal subtraction technique to remove normal structures such as pulmonary vessels and bones..Digital subtraction angiography (DSA) is inadequate for coronary artery due to the existence of severe motion artifacts. In view of this, we hav作者: overrule 時(shí)間: 2025-3-29 17:33
Kenji SuzukiPresents computational intelligence technology uses in medical image analysis.Examines medical decision making based on biomedical images.Covers the state-of-the-art research and technologies in compu作者: 搬運(yùn)工 時(shí)間: 2025-3-29 22:19
http://image.papertrans.cn/c/image/232469.jpg作者: 真 時(shí)間: 2025-3-29 23:59 作者: intertwine 時(shí)間: 2025-3-30 06:06 作者: 柔軟 時(shí)間: 2025-3-30 10:56 作者: Lasting 時(shí)間: 2025-3-30 13:46 作者: 文件夾 時(shí)間: 2025-3-30 19:25 作者: 吃掉 時(shí)間: 2025-3-30 23:20
