2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)最新文献_第2页

Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation 基于插值的灰度共生矩阵计算纹理方向性估计

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563413

Marcin Kociolek, P. Bajcsy, M. Brady, Antonio Cardone

引用次数: 1

Vehicle detector training with labels derived from background subtraction algorithms in video surveillance 基于背景减法算法的车辆检测器训练

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563368

Sebastian Cygert, A. Czyżewski

引用次数: 3

Deep Learning for Natural Language Processing and Language Modelling 自然语言处理和语言建模的深度学习

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563389

P. Kłosowski

引用次数: 35

On Using Quaternionic Rotations for Indpendent Component Analysis 四元数旋转在独立成分分析中的应用

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563269

A. Borowicz

引用次数: 3

Noise Cancellation Method for Speech Signal by Using an Extension Type UKF 基于扩展型UKF的语音信号降噪方法

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563312

H. Orimoto, A. Ikuta

引用次数: 0

An application of acoustic sensors for the monitoring of road traffic 声学传感器在道路交通监测中的应用

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563406

Karolina Marciniuk, M. Szczodrak, A. Czyżewski

引用次数: 3

Centerline-Radius Polygonal-Mesh Modeling of Bifurcated Blood Vessels in 3D Images using Conformal Mapping 采用保角映射的中心线-半径多边形网格三维图像中分叉血管的建模

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563388

C. Vinhais, M. Kociński, A. Materka

{"title":"Centerline-Radius Polygonal-Mesh Modeling of Bifurcated Blood Vessels in 3D Images using Conformal Mapping","authors":"C. Vinhais, M. Kociński, A. Materka","doi":"10.23919/SPA.2018.8563388","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563388","url":null,"abstract":"Accurate modeling of the human vascular tree from 3D computed tomography (CTA) or magnetic resonance (MRA) angiograms is required for visualization, diagnosis of vascular diseases, and computational fluid dynamic (CFD) blood flow simulations. This work describes an automated algorithm for constructing the polygonal mesh of blood vessels from such images. Each vascular segment is modeled as a tubular object, and a thin plate spline transform is used to generate the corresponding surface from its centerline-radius representation. A novel approach for generating the polygonal mesh of bifurcating vessels based on conformal mapping is presented. A mathematical description of the methodology is also provided. The model is improved by computing local intensity features with subvoxel accuracy, to slightly deform the mesh of the vascular tree for fine-tuning. The proposed algorithm was successfully tested on a 3D synthetic image containing randomly generated vascular branches. Experiment results, confirmed by real-world Time of Flight MRA, demonstrate that our methodology is consistent and capable of generating high quality triangulated meshes of vascular trees, suitable for further CFD simulations. Compared to common techniques, conformal mapping proved to be a simple and effective mathematical approach for polygonal mesh modeling of bifurcating vessels.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114152817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An artificial neural network for GFR estimation in the DCE-MRI studies of the kidneys 用于肾脏DCE-MRI研究中GFR估计的人工神经网络

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563412

M. Strzelecki, A. Klepaczko, Martyna Muszelska, E. Eikefjord, J. Rørvik, A. Lundervold

{"title":"An artificial neural network for GFR estimation in the DCE-MRI studies of the kidneys","authors":"M. Strzelecki, A. Klepaczko, Martyna Muszelska, E. Eikefjord, J. Rørvik, A. Lundervold","doi":"10.23919/SPA.2018.8563412","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563412","url":null,"abstract":"The dynamic contrast-enhanced magnetic resonance imaging is a diagnostic method directed at estimation of renal performance. Analysis of the image intensity time-courses in the renal cortex and parenchyma enables quantification of the kidney filtration characteristics. A standard approach used for that purpose involves fitting a pharmacokinetic model to image data and optimizing a set of model parameters. It is essentially a multi-objective and non-linear optimization problem. Standard methods applied in such scenarios include nonlinear least-squares (NLS) algorithms, such as Levenberg-Marquardt or Trust Region Reflective methods. The major disadvantage of these classical approaches is the requirement for determining the starting point of the optimization, whose final result is a local minimum of the objective function. On the contrary, artificial neural networks (ANN) are trained based on a large range of parameter combinations, potentially covering whole solution space. Thus, they appear particularly useful in fitting complex, non-linear, multi-parametric relationships to the observed noisy data and offer greater ability to detect all possible interactions between predictor variables without the need for explicit statistical formulation. In this paper we compare the ANN and NLS approaches in application to measuring perfusion based on DCE-MR images. The experiments performed on a dataset containing 10 dynamic image series collected for 5 healthy volunteers proved superior performance of the neural networks over classical methods in terms of quantifying true perfusion parameters, robustness to noise and varying imaging conditions.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dictionary-based through-plane interpolation of prostate cancer T2-weighted MR images 基于字典的前列腺癌t2加权MR图像的平面插值

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563411

Jakub Jurek, M. Kociński, A. Materka, Are Losnegård, L. Reisæter, O. Halvorsen, C. Beisland, J. Rørvik, A. Lundervold

{"title":"Dictionary-based through-plane interpolation of prostate cancer T2-weighted MR images","authors":"Jakub Jurek, M. Kociński, A. Materka, Are Losnegård, L. Reisæter, O. Halvorsen, C. Beisland, J. Rørvik, A. Lundervold","doi":"10.23919/SPA.2018.8563411","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563411","url":null,"abstract":"T2-weighted magnetic resonance images (T2W MRI) of prostate cancer are usually acquired with a large slice thickness compared to in-plane voxel dimensions and to the minimal significant malignant prostate tumour size. This causes a negative partial volume effect, decreasing the precision of tumour volumetry and complicating 3D texture analysis of the images. At the same time, three orthogonal, anisotropic acquisitions with overlapping fields of view are often acquired to allow insight into the prostate from different anatomical planes. It is desirable to reconstruct an isotropic prostate T2W image, using the 3 orthogonal volumes computationally, instead of directly acquiring a high-resolution MR image, which typically requires elongated scanning time, with higher cost, less patient comfort and lower signal-to-noise ratio. In our previous work, we followed the above rationale applying a Markov-Random-Field(MRF)-based combination of 3 orthogonal T2W images of the prostate. Our initial results were, however, biased by the quality of input orthogonal images. These were first preprocessed using spline interpolation to yield the same voxel dimensions and later registered. In this paper, we apply a dictionary learning approach to interpolation in order to increase the resolution of a coronal T2W MRI image. We compose a low-resolution dictionary from the original axial image, calculate its sparse representation by Orthogonal Matching Pursuit and finally derive the high-resolution dictionary to improve the original coronal image. We assess the improvement in visual image quality as satisfying and propose further studies.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129950552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Methods of Enriching The Flow of Information in The Real-Time Semantic Segmentation Using Deep Neural Networks 基于深度神经网络的实时语义分割信息流丰富方法

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563422

J. Bednarek, K. Piaskowski, Michał Bednarek

引用次数: 0