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Sean Patrick Morrissey

Bio: Sean Patrick Morrissey is an academic researcher from University of Rennes. The author has contributed to research in topics: Segmentation & Non-local means. The author has an hindex of 6, co-authored 8 publications receiving 437 citations.

Papers
More filters
Book ChapterDOI
06 Sep 2008
TL;DR: This adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.
Abstract: Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR) This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data Validation is performed on synthetic data and on real data for both conventional MR images and DT images Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI

287 citations

Book ChapterDOI
29 Oct 2007
TL;DR: The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.
Abstract: Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed in this paper are based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. We introduce three variations of the NL-means algorithms adapted to DW-MRI and to DT-MRI. Experiments were carried out on a set of 12 diffusion-weighted images (DW-MRI) of the same subject. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.

88 citations

01 Sep 2008
TL;DR: A fully automatic workflow for Multiple Sclerosis lesion segmentation is described, where no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand.
Abstract: A fully automatic workflow for Multiple Sclerosis (MS) lesion segmentation is described. Fully automatic means that no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand. Our workflow is composed of three steps: an intensity inhomogeneity (IIH) correction, skull-stripping and MS lesions segmentation. A validation comparing our results with two experts is done on MS MRI datasets of 24 MS patients from two different sites.

32 citations

01 Sep 2008
TL;DR: In this paper, a new algorithm for segmentation of white matter lesions and normal appearing brain tissues in Multiple Sclerosis (MS) was presented, where two different segmentation methods are combined in order to have a better and more meaningful segmentation.
Abstract: A new algorithm for segmentation of white matter lesions and normal appearing brain tissues in Multiple Sclerosis (MS) is presented. Two different segmentation methods are combined in order to have a better and more meaningful segmentation. On the one hand, a local segmentation approach, the Mean Shift, is used to generate local regions in our images. On the other hand, a variant of the Expectation Maximization is employed to classify these regions as Normal Appearing Brain Tissues (NABT) or lesions. Validation of this method is performed with synthetic and real data. The output is a more powerful algorithm that employs at the same time global and local information to improve image segmentation.

31 citations

06 Sep 2008
TL;DR: A new tool for segmenting multiple sclerosis lesions that can take advantage of the complementary modalities and provide a powerful and accurate tool based on the integration of multi channel information in a scale-space paradigm is presented.
Abstract: We present a new tool for segmenting multiple sclerosis lesions that can take advantage of the complementary modalities we usually use for this purpose. Based on the integration of multi channel information in a scale-space paradigm, its optimization by graph cuts provides a powerful and accurate tool. After presenting the mathematical and physical background on which is based the spectral gradient, we introduce its computational optimization by the graph cuts approach. The validation on synthetic and real data shows both its accuracy and reliability on different sets of standard MRI of sequences.

17 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The physics of DW-MRI is reviewed, currently preferred methodology is indicated, and the limits of interpretation of its results are explained, with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience.

2,027 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter, which is automatically obtained from the images using a new local noise estimation method.
Abstract: PURPOSE: To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). MATERIALS AND METHODS: Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. RESULTS: The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. CONCLUSION: The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.

871 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.
Abstract: We present an extension of the BM3D filter to volumetric data. The proposed algorithm, BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d -dimensional patches are stacked together in a (d+1) -dimensional array and jointly filtered in transform domain. While in BM3D the basic data patches are blocks of pixels, in BM4D we utilize cubes of voxels, which are stacked into a 4-D “group.” The 4-D transform applied on the group simultaneously exploits the local correlation present among voxels in each cube and the nonlocal correlation between the corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations. We evaluate the algorithm on denoising of volumetric data corrupted by Gaussian and Rician noise, as well as on reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.

748 citations

Journal ArticleDOI
TL;DR: Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation in quantitative magnetic resonance analysis.

709 citations

Journal ArticleDOI
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Abstract: The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove fine structures in images. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms and, second, to propose a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the “method noise,” defined as the difference between a digital image and its denoised version. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The denoising performance of all considered methods is compared in four ways; mathematical: asymptotic order of magnitude of the method noise under regularity assumptions; perceptual-mathematical: the algorithms artifacts and their explanation as a violation of the image model; quantitative experimental: by tables of $L^2$ distances of the denoised version to the original image. The fourth and perhaps most powerful evaluation method is, however, the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

445 citations