Nonrigid Registration of Joint Histograms for Intensity Standardization in Magnetic Resonance Imaging
F. Jager,Joachim Hornegger +1 more
TLDR
A novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale, which is the first approach that uses the properties of all acquired images jointly.Abstract:
A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.read more
Citations
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Fast and robust multi-atlas segmentation of brain magnetic resonance images.
Jyrki Lötjönen,Robin Wolz,Juha Koikkalainen,Lennart Thurfjell,Gunhild Waldemar,Hilkka Soininen,Daniel Rueckert +6 more
TL;DR: An optimised pipeline for multi-atlas brain MRI segmentation is introduced and intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time.
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Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.
Chengwen Chu,Daniel L. Belavý,Daniel L. Belavý,Gabriele Armbrecht,Martin Bansmann,Dieter Felsenberg,Guoyan Zheng +6 more
TL;DR: A learning-based, unified random forest regression and classification framework to tackle the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images is proposed.
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Efficient and robust model-to-image alignment using 3D scale-invariant features
TL;DR: Feature-Based Alignment (FBA) as mentioned in this paperBA is a general method for efficient and robust model-to-image alignment, where features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution.
Journal ArticleDOI
Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions
Xiaofei Sun,Lin Shi,Yishan Luo,Wei Yang,Wei Yang,Hongpeng Li,Peipeng Liang,Kuncheng Li,Vincent Mok,Winnie C.W. Chu,Defeng Wang +10 more
TL;DR: A histogram-based MRI intensity normalization method is proposed that can normalize scans which were acquired on different MRI units and can create a higher quality Chinese brain template.
Journal ArticleDOI
Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation
Cheng Chen,Daniel L. Belavy,Weimin Yu,Chengwen Chu,Gabriele Armbrecht,Martin Bansmann,Dieter Felsenberg,Guoyan Zheng +7 more
TL;DR: This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images with validated method on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies and achieves better or comparable results.
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