scispace - formally typeset
Search or ask a question
Author

Sir Michael Brady

Bio: Sir Michael Brady is an academic researcher from University of Oxford. The author has contributed to research in topics: Image registration & Mammography. The author has an hindex of 13, co-authored 59 publications receiving 1332 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: A modality independent neighbourhood descriptor (MIND), based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising, is proposed and applied for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans.

580 citations

Book ChapterDOI
16 Jun 2010
TL;DR: Results on 2,217 GE digital images, from a diversity of sites, show encouraging agreement with MRI data, as well as robustness to noise and errors in the imaging physics data.
Abstract: Volumetric breast composition measurements generally require accurate imaging physics data In this paper we describe a new method (VolparaTM) that uses relative (as opposed to absolute) physics modeling together with additional information derived from the image to substantially reduce the dependence on imaging physics data Results on 2,217 GE digital images, from a diversity of sites, show encouraging agreement with MRI data, as well as robustness to noise and errors in the imaging physics data.

195 citations

Book ChapterDOI
22 Sep 2013
TL;DR: An efficient quantised representation is derived that enables very fast computation of point-wise distances between descriptors and is evaluated for the registration of 3D ultrasound and MRI brain scans for neurosurgery.
Abstract: Image-guided interventions often rely on deformable multimodal registration to align pre-treatment and intra-operative scans. There are a number of requirements for automated image registration for this task, such as a robust similarity metric for scans of different modalities with different noise distributions and contrast, an efficient optimisation of the cost function to enable fast registration for this time-sensitive application, and an insensitive choice of registration parameters to avoid delays in practical clinical use. In this work, we build upon the concept of structural image representation for multi-modal similarity. Discriminative descriptors are densely extracted for the multi-modal scans based on the "self-similarity context". An efficient quantised representation is derived that enables very fast computation of point-wise distances between descriptors. A symmetric multi-scale discrete optimisation with diffusion reguIarisation is used to find smooth transformations. The method is evaluated for the registration of 3D ultrasound and MRI brain scans for neurosurgery and demonstrates a significantly reduced registration error (on average 2.1 mm) compared to commonly used similarity metrics and computation times of less than 30 seconds per 3D registration.

142 citations

Journal ArticleDOI
TL;DR: An approach that enables accurate voxel-wise deformable registration of high-resolution 3D images without the need for intermediate image warping or a multi-resolution scheme is proposed, and significant improvements in registration accuracy are shown when using the additional information provided by the registration uncertainty estimates.

74 citations

Book ChapterDOI
26 Sep 2004
TL;DR: In this paper, a 3D, patient-specific, anatomically accurate, finite element model of the breast using MR images was developed, which can be deformed in a physically realistic manner using nonlinear elasticity theory to simulate the breast during mammography.
Abstract: Two of the major imaging modalities used to detect and monitor breast cancer are (contrast enhanced) magnetic resonance (MR) imaging and mammography Image fusion, including accurate registration between MR images and mammograms, or between CC and MLO mammograms, is increasingly key to patient management (for example in the multidisciplinary meeting), but registration is extremely difficult because the breast shape varies massively between the modalities, due both to the different postures of the patient for the two modalities and to the fact that the breast is forcibly compressed during mammography In this paper, we develop a 3D, patient-specific, anatomically accurate, finite element model of the breast using MR images, which can be deformed in a physically realistic manner using nonlinear elasticity theory to simulate the breast during mammography

59 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This paper attempts to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain, and provides an extensive account of registration techniques in a systematic manner.
Abstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

1,434 citations

Journal ArticleDOI
TL;DR: A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

1,053 citations

Journal ArticleDOI
TL;DR: A modality independent neighbourhood descriptor (MIND), based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising, is proposed and applied for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans.

580 citations

Journal ArticleDOI
TL;DR: The utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity is demonstrated.

539 citations

Journal ArticleDOI
TL;DR: This paper trains a fully convolutional network to generate a target image given a source image and proposes to use the adversarial learning strategy to better model the FCN, designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images.
Abstract: Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.

417 citations