Machine learning and radiology
S Wang, RM Summers - Medical image analysis, 2012 - Elsevier
In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …
radiology. We focused on six categories of applications in radiology: medical image …
Methods on skull stripping of MRI head scan images—a review
P Kalavathi, VBS Prasath - Journal of digital imaging, 2016 - Springer
The high resolution magnetic resonance (MR) brain images contain some non-brain tissues
such as skin, fat, muscle, neck, and eye balls compared to the functional images namely …
such as skin, fat, muscle, neck, and eye balls compared to the functional images namely …
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation
Unsupervised domain adaptation has attracted growing research attention on semantic
segmentation. However, 1) most existing models cannot be directly applied into lesions …
segmentation. However, 1) most existing models cannot be directly applied into lesions …
elastix: A Toolbox for Intensity-Based Medical Image Registration
Medical image registration is an important task in medical image processing. It refers to the
process of aligning data sets, possibly from different modalities (eg, magnetic resonance …
process of aligning data sets, possibly from different modalities (eg, magnetic resonance …
A hybrid approach to the skull stripping problem in MRI
We present a novel skull-stripping algorithm based on a hybrid approach that combines
watershed algorithms and deformable surface models. Our method takes advantage of the …
watershed algorithms and deformable surface models. Our method takes advantage of the …
Robust brain extraction across datasets and comparison with publicly available methods
JE Iglesias, CY Liu, PM Thompson… - IEEE transactions on …, 2011 - ieeexplore.ieee.org
Automatic whole-brain extraction from magnetic resonance images (MRI), also known as
skull stripping, is a key component in most neuroimage pipelines. As the first element in the …
skull stripping, is a key component in most neuroimage pipelines. As the first element in the …
A review of atlas-based segmentation for magnetic resonance brain images
Normal and abnormal brains can be segmented by registering the target image with an
atlas. Here, an atlas is defined as the combination of an intensity image (template) and its …
atlas. Here, an atlas is defined as the combination of an intensity image (template) and its …
BEaST: brain extraction based on nonlocal segmentation technique
Brain extraction is an important step in the analysis of brain images. The variability in brain
morphology and the difference in intensity characteristics due to imaging sequences make …
morphology and the difference in intensity characteristics due to imaging sequences make …
[图书][B] 2-D and 3-D image registration: for medical, remote sensing, and industrial applications
AA Goshtasby - 2005 - books.google.com
To master the fundamentals of image registration, there is no more comprehensive source
than 2-D and 3-D Image Registration. In addition to delving into the relevant theories of …
than 2-D and 3-D Image Registration. In addition to delving into the relevant theories of …