A robust similarity measure for volumetric image registration with outliers

Snape, Patrick; Pszczółkowski, Stefan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Ledig, Christian; Rueckert, Daniel

doi:10.1016/j.imavis.2016.05.006

Cited by 8 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All rigid intra-subject registrations were performed using Normalised Mutual Information (NMI) [ 67 ] with 64 histogram bins as similarity measure. The non-linear registrations of the age-specific T1 template to T1 subject sequences used a cosine similarity measure based on normalised gradient fields (cosNGF) [ 68 ], a transformation model based on B-spline free-form deformations [ 69 ], an image pyramid of 4 levels, bending energy (BE) as regularisation term, and 5 mm final control point spacing. The energy term weight distribution was set to 0.995 cosNGF +0.005 BE.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Pszczółkowski

Law

Gallagher

et al. 2019

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

Background Spontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH. Methods We take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation. Results Using Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way. Conclusion Our technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The energy term weight distribution was set to 0.995 cosNGF +0.005 BE. The cosine similarity measure that we use is designed to be much less sensitive than standard similarities to missing correspondences [ 68 ], such as those introduced by the presence of haematoma and oedema.…”

Section: Methodsmentioning

confidence: 99%

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Pszczółkowski

Law

Gallagher

et al. 2019

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their features are extracted from an image by convolving the image using a chosen filter, such as a Kernel filter or Gabor filter, or combining them to generate a feature map of the same input image [48]. Measuring the similarity can be done by applying a cosine similarity function [49].…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

View full text Add to dashboard Cite

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

show abstract

“…Image registration is a computational task that determines the spatial correspondence between two images of the same object acquired at different angles, at different times, using different image modalities, or under different acquisition conditions [2,3,4]. In general, an image registration method can be decomposed into three parts: building a transformation model, computing a similarity measure and performing the optimization of the registration model [4,5]. Transformation models, such as rigid or non-rigid models, delineate the transformation that can be used to represent the underlying correspondences.…”

Section: Introductionmentioning

confidence: 99%

“…Rigid models describe simple linear mappings such as translations, rotations, scalings and shears. However, non-rigid transformation models can represent more complex mappings, since local deformations are also taken into account, resulting thus in longer computation times [6,5]. Non-rigid image registration is an extensive research field, encompassing many applications and several specific algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimizing a medical image registration algorithm based on profiling data for real-time performance

Gulo

Sementille

Tavares

2021

Multimed Tools Appl

View full text Add to dashboard Cite

Image registration is a commonly task in medical image analysis.Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task; however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open

show abstract

A robust similarity measure for volumetric image registration with outliers

Cited by 8 publications

References 42 publications

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Optimizing a medical image registration algorithm based on profiling data for real-time performance

Contact Info

Product

Resources

About