Titlebook: Computational Intelligence in Biomedical Imaging; Kenji Suzuki Book 2014 Springer Science+Business Media New York 2014 artificial neural n

果園

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

Ordnance

Wordlist

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

隱士

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

DNR215

FEAS

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-

FEAS

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

與野獸博斗者

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

打谷工具

Carcinogen

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