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1st international SIPAIM workshop on processing and Analysis of Biomedical Information, SaMBa 2018 held in Conjunction with Medical image Computing and Computer-Assisted Intervention, MICCAI 2018

作者： Barrera, Cristian Corredor, Germán Alfonso, Sunny Mosquera, Andrés Romero, Eduardo CIM@LAB Universidad Nacional de Colombia Bogota Colombia

ISBN: (纸本)9783030138349

Analysis of tubular glands plays an important role for gastric cancer diagnosis, grading, and prognosis;however, gland quantification is a highly subjective task, prone to error. Objective identification of glans might help clinicians for analysis and treatment planning. The visual characteristics of such glands suggest that information from nuclei and their context would be useful to characterize them. In this paper we present a new approach for segmentation of gland nuclei based on nuclear local and contextual (neighborhood) information. A Gradient-Boosted-Regression-Trees classifier is trained to distinguish between gland-nuclei and non-gland-nuclei. Validation was carried out using a dataset containing 45702 annotated nuclei from 90 1024 × 1024 fields of view extracted from gastric cancer whole slide images. A Deep Learning model was trained as a baseline. Results showed an accuracy and f-score 5.4% and 23.6% higher, respectively, with the presented framework than with the Deep Learning approach. © Springer Nature Switzerland AG 2019.

关键词： Grading

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Refraction-Aware Integral Operator for Speed-of-Sound Pulse-Echo Imaging

Refraction-Aware Integral Operator for Speed-of-Sound Pulse-...

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IEEE international Ultrasonics Symposium

作者： Samuel Beuret Dimitris Perdios Jean-Philippe Thiran Signal Processing Laboratory (LTS5) École polytechnique fédérale de Lausanne Lausanne Switzerland University Hospital Center (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154497

Speed-of-sound pulse-echo imaging denotes a set of promising techniques able to expand the diagnostic capabilities of medical ultrasound. Their objective is to recover a speed-of-sound map of soft tissue and use it as a biomarker for diseases that are difficult to detect using standard B-mode imaging. Such methods often rely on a specific linear operator based on straight-ray approximation to compute the local wave propagation time. Refraction effects are thus neglected, leading to potential artifacts in the reconstructed speed-of-sound maps. We propose in this paper a non-linear, iterative operator able to account for these effects with a low computational complexity. In order to assess the accuracy of the proposed operator, we first compare its predictions to the results of k-Wave simulations. A model based on the proposed operator is then deployed to recover speed-of-sound maps from simulated data. The results are compared to those obtained by a standard linear operator, and a mitigation of the reconstruction artifacts due to straight-ray approximation is observed. It implies in particular a reduction of the RMSE from 11.65 m/s to 7.08 m/s in a low-noise case.

关键词： Ultrasonic imaging Computational modeling Propagation Predictive models Computational complexity image reconstruction Standards

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non-local Super Resolution in Ultrasound Imaging 20

Non-local Super Resolution in Ultrasound Imaging

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20th IEEE international workshop on Multimedia Signal processing (MMSP)

作者： Khavari, Parviz Asif, Amir Rivaz, Hassan Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781538660706

The resolution of ultrasound (US) images is limited by physical constraints and hardware restrictions, such as the frequency, width and focal zone of the US beam. Different interpolation methods are often used to increase the sampling rate of ultrasound images. However, interpolation methods generally introduce blur in images. Herein, we present a super resolution (SR) algorithm for reconstruction of the B-mode images using the information from the envelope of radio frequency (RF) data. Our method is based on utilizing repetitive data in the non local neighborhood of samples. The performance of the proposed approach is determined both qualitatively and quantitatively using phantom and in-vivo data.

关键词： Interpolation image resolution Radio frequency Ultrasonic imaging image reconstruction Phantoms Bayes methods

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local 3D Point Cloud Denoising via Bipartite Graph approximation & Total Variation 20

Local 3D Point Cloud Denoising via Bipartite Graph Approxima...

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20th IEEE international workshop on Multimedia Signal processing (MMSP)

作者： Dinesh, Chinthaka Cheung, Gene Bajic, Ivan, V Yang, Cheng Simon Fraser Univ Burnaby BC Canada Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781538660706

Acquired 3D point cloud data, whether from active sensors directly or from stereo-matching algorithms indirectly, typically contain non-negligible noise. To address the point cloud denoising problem, we propose a local graph-based algorithm. Specifically, given a k-nearest-neighbor graph of the 3D points, we first approximate it with a bipartite graph (independent sets of red and blue nodes) using a KL divergence criterion. For each partite of nodes (say red), we first define surface normal of each red node using 3D coordinates of neighboring blue nodes, so that red node normals n can be written as a linear function of red node coordinates p. We then formulate a convex optimization problem, with a quadratic fidelity term parallel to p - q parallel to(2)(2) given noisy observed red coordinates q and a graphtotal variation (GTV) regularization term for surface normals of neighboring red nodes. We minimize the resulting l(2)-l(1)-norm using alternating direction method of multipliers (ADMM) and proximal gradient descent. The two partites of nodes are alternately optimized until convergence. Experimental results show that compared to state-of-the-art schemes with similar complexity, our proposed algorithm achieves the best overall denoising performance objectively and subjectively.

关键词： graph signal processing point cloud denoising total variation bipartite graph approximation

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Color noise-based feature for splicing detection and localization 20

Color noise-based feature for splicing detection and localiz...

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20th IEEE international workshop on Multimedia Signal processing (MMSP)

作者： Destruel, Christophe Itier, Vincent Strauss, Olivier Puech, William Univ Montpellier CNRS LIRMM Montpellier France

ISBN: (纸本)9781538660706

images that have been altered and more specifically spliced together have invaded the digital domain due to the ease with which we are able to copy and paste them. To detect such forgeries the digital image processing community is proposing new automatic algorithms designed to help human operators reveal manipulated images. In this paper, we focus on a local detection system, which considers which tampered areas produce local statistical effects that do not impact neighboring areas or the image as a whole. We propose to study how the definition of local blocks, considering their size and overlap, impacts final pixel detection. We also propose new features which are an original way to consider the noise of an image as a colored signal. Indeed, in a non-forged image, there is a high correlation of noise between the three color channels R, G and B. We show that an optimal configuration can be defined and in this case the proposed approach outperforms several previously proposed methods using the same tested dataset, in uncompressed and JPEG modes. Note, in this paper we only focus on feature extraction without using machine learning.

关键词： Microsoft Windows Feature extraction image coding Colored noise Transform coding image color analysis Splicing

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Mixed-model noise removal in 3d via rotation-and-scale invariant non-local means 1st

Mixed-model noise removal in 3d via rotation-and-scale invar...

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1st international SIPAIM workshop on processing and Analysis of Biomedical Information, SaMBa 2018 held in Conjunction with Medical image Computing and Computer-Assisted Intervention, MICCAI 2018

作者： Liu, Xiangyuan Liu, Quansheng Wu, Zhongke Wang, Xingce Sole, Jose Pozo Frangi, Alejandro Beijing Normal University Beijing China Brittany France University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030138349

Mixed noise is a major issue influencing quantitative analysis in different forms of magnetic resonance image (MRI), such as T1 and diffusion image like DWI and DTI. Using different filters sequentially to remove mixed noise will severely deteriorate such medical images. We present a novel algorithm called rotation-and-scale invariant nonlocal means filter (RSNLM) to simultaneously remove mixed noise from different kinds of three-dimensional (3D) MRI images. First, we design a new similarity weights, including rank-ordered absolute difference (ROAD), coming from a trilateral filter (TriF) that is obtained to detect the mixed and high-level noise. Then, we present a shape view to consider the MRI data as a 3D operator, with which the similarity between the patches is calculated with the rigid transformation. The translation, rotation and scale have no influence on the similarity. Finally, the adaptive parameter estimation method of ROAD is illustrated, and the effective proof that validates the proposed algorithm is presented. Experiments using synthetic data with impulse noise, Rician noise, and the real MRI data confirm that the proposed method yields superior performance compared with current state-of-the-art methods. © Springer Nature Switzerland AG 2019.

关键词： Impulse noise

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HYPERSPECTRAL DENOISING VIA CROSS TOTAL VARIATION-REGULARIZED UNIDIRECTIONAL nonlocal LOW-RANK TENSOR approximation 25

HYPERSPECTRAL DENOISING VIA CROSS TOTAL VARIATION-REGULARIZE...

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25th IEEE international Conference on image processing (ICIP)

作者： Sun, Le Jeon, Byeungwoo Wu, Zebin Xiao, Liang Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Sungkyunkwan Univ Sch Elect & Elect Engn Suwon 440746 South Korea Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781479970612

In this paper, we propose a novel cross total variation regularized unidirectional nonlocal low rank tensor approximation method for hyperspectral image denoising. It fully explores the spectral-spatial correlation and non-local self-similarity simultaneously in tensor case and points out that the nonlocal self-similarity is the most important for precisely restoring the HSI. Following the research line in [1], we propose to embed the cross total variation (CrTV) regularization into the unidirectional low rank tensor framework to alleviate the common consistency issue of pixels in overlapped regions. CrTV shows great power to explore the spatial-spectral correlation and has great ability to keep the fine spatial details and preserve the spectra in the course of HSI denoising. The final model can be effectively solved by the alternating direction methods of multipliers (ADMM). Experimental results on HSI data sets validate that the complementary priors (i.e., spatial-spectral correlation and non local self-similarity) really contribute to the performance and also illustrate the superiority of the proposed method when compared with other state-of-the-art denoising methods.

关键词： Hyperspectral denoising unidirectional low rank tensor cross total variation spatial-spectral correlation non-local self-similarity

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Joint Sparse Recovery of Misaligned Multimodal images via Adaptive local and nonlocal Cross-Modal Regularization

Joint Sparse Recovery of Misaligned Multimodal Images via Ad...

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IEEE international workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMSAP)

作者： Nasser Eslahi Alessandro Foi Tampere University Finland

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

Given few noisy linear measurements of distinct misaligned modalities, we aim at recovering the underlying multimodal image using a sparsity promoting algorithm. Unlike previous multimodal sparse recovery approaches employing side information under the naive assumption of perfect calibration of modalities or of known deformation parameters, we adaptively estimate the deformation parameters from the images separately recovered from the incomplete measurements. We develop a multiscale dense registration method that proceeds alternately by finding block-wise intensity mapping models and a shift vector field which is used to obtain and refine the deformation parameters through a weighted least-squares approximation. The co-registered images are then jointly recovered in a plug-and-play framework where a collaborative filter leverages the local and nonlocal cross-modal correlations inherent to the multimodal image. Our experiments with this fully automatic registration and joint recovery pipeline show a better detection and sharper recovery of fine details which could not be separately recovered.

关键词： Strain Imaging Noise measurement Adaptation models Optimization Deformable models Signal to noise ratio

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non-local Functionals Related to the Total Variation and Connections with image processing

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Annals of PDE 2018年第1期4卷 9页

作者： Brezis, Haïm Nguyen, Hoai-Minh Department of Mathematics Hill Center Busch Campus Rutgers University 110 Frelinghuysen Road Piscataway 08854 NJ United States Departments of Mathematics and Computer Science Technion Israel Institute of Technology Haifa 32.000 Israel Laboratoire Jacques-Louis Lions UPMC 4 Place Jussieu Paris 75005 France EPFL SB MATHAA CAMA Station 8 Lausanne 1015 Switzerland

We present new results concerning the approximation of the total variation, ∫ Ω| ∇ u| , of a function u by non-local, non-convex functionals of the form Λδ(u)=∫Ω∫Ωδφ(|u(x)-u(y)|/δ)|x-y|d+1dxdy,as δ→ 0 , w... 详细信息

We present new results concerning the approximation of the total variation, ∫ _Ω| ∇ u| , of a function u by non-local, non-convex functionals of the form Λδ(u)=∫Ω∫Ωδφ(|u(x)-u(y)|/δ)|x-y|d+1dxdy,as δ→ 0 , where Ω is a domain in R^d and φ: [0 , + ∞) → [0 , + ∞) is a non-decreasing function satisfying some appropriate conditions. The mode of convergence is extremely delicate and numerous problems remain open. De Giorgi’s concept of Γ -convergence illuminates the situation, but also introduces mysterious novelties. The original motivation of our work comes from image processing. © 2018, Springer international Publishing AG, part of Springer Nature.

关键词： Bounded variation non-convex functional non-local functional Sobolev spaces Total variation Γ -Convergence

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A Global-local Embedding Module for Fashion Landmark Detection

A Global-Local Embedding Module for Fashion Landmark Detecti...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Sumin Lee Sungchan Oh Chanho Jung Changick Kim Korea Advanced Institute of Science and Technology(KAIST) Electronics and Telecommunications Research Institute Hanbat National University

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Detecting fashion landmarks is a fundamental technique for visual clothing analysis. Due to the large variation and non-rigid deformation of clothes, localizing fashion landmarks suffers from large spatial variances across poses, scales, and styles. Therefore, understanding contextual knowledge of clothes is required for accurate landmark detection. To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module. The global-local embedding module is based on a non-local operation for capturing long-range dependencies and a subsequent convolution operation for adopting local neighborhood relations. With this processing, the network can consider both global and local contextual knowledge for a clothing image. We demonstrate that our proposed method has an excellent ability to learn advanced deep feature representations for fashion landmark detection. Experimental results on two benchmark datasets show that the proposed network outperforms the state-of-the-art methods.

关键词： Clothing Feature extraction Conferences Convolution Computer vision Heating systems Benchmark testing

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