Ballerina
发表于 2025-3-23 12:06:57
A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmis a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior tak
不能妥协
发表于 2025-3-23 16:48:52
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aesthetician
发表于 2025-3-23 21:07:41
A Bayesian Approach for Liver Analysis: Algorithm and Validation Studys. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. The metho
Mirage
发表于 2025-3-24 01:48:46
Classification of Suspected Liver Metastases Using fMRI Images: A Machine Learning Approachased statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics are evaluated from .-W fMRI images acquired during the breathing of air, air-.., and carbogen. A classification model is build to differentiate betwee
不安
发表于 2025-3-24 05:43:05
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giggle
发表于 2025-3-24 07:14:52
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摄取
发表于 2025-3-24 10:45:59
MRI Bone Segmentation Using Deformable Models and Shape Priors method is the combination of physically-based deformable models with shape priors. Models evolve under the influence of forces that exploit image information and prior knowledge on shape variations. The prior defines a Principal Component Analysis (PCA) of global shape variations and a Markov Rando
受人支配
发表于 2025-3-24 18:53:19
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Evolve
发表于 2025-3-24 21:36:52
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他一致
发表于 2025-3-25 02:13:09
Toward Unsupervised Classification of Calcified Arterial Lesionsthe challenging problem of unsupervised calcified lesion classification. We propose an algorithm, . (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a