2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)最新文献_第6页

Low band continuous speech system for voice pathologies identification 语音病理识别的低频带连续语音系统

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563393

Hugo Cordeiro, C. Meneses

引用次数: 5

Hierarchical Feature-learning Graph-based Segmentation of Fat-Water MR Images 基于分层特征学习的脂肪-水磁共振图像分割

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563415

Faezeh Fallah, Bin Yang, S. Walter, F. Bamberg

{"title":"Hierarchical Feature-learning Graph-based Segmentation of Fat-Water MR Images","authors":"Faezeh Fallah, Bin Yang, S. Walter, F. Bamberg","doi":"10.23919/SPA.2018.8563415","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563415","url":null,"abstract":"In this paper, we proposed a deformation-Iregistration-free method for multilabel segmentation of fat-water MR images without need to prior localization or geometry estimation. This method employed a multiresolution (hierarchical) feature- and prior-based Random Walker graph and a hierarchical conditional random field (HCRF). To incorporate both aspatial (intra-patch) and spatial (inter-patch neighborhood) information into the image segmentation, the proposed random walker graph was made of a multiresolution spatial and a multiresolution aspatial (prior-based) sub-graph. Edge weights and prior probabilities of this graph as well as the energy terms of the HCRF were determined by a hierarchical random decision forest classifier. This classifier was trained using multiscale local and contextual features extracted from fat-water (2-channel) magnetic resonance (MR) images. The proposed method was trained and evaluated for simultaneous volumetric segmentation of vertebral bodies and intervertebral discs on fat-water MR images. These evaluations revealed its comparable accuracy to the state-of-the-art while demanding less computations and training data. The proposed method was, however, generic and extendible for segmenting any kind of tissues on other multichannel images.","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":"125517478","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: 5

Numerical simulation of the b-SSFP sequence in MR perfusion-weighted imaging of the kidney 肾MR灌注加权成像中b-SSFP序列的数值模拟

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563339

A. Klepaczko, P. Skulimowski, M. Strzelecki, L. Stefanczyk, E. Eikefjord, J. Rørvik, A. Lundervold

{"title":"Numerical simulation of the b-SSFP sequence in MR perfusion-weighted imaging of the kidney","authors":"A. Klepaczko, P. Skulimowski, M. Strzelecki, L. Stefanczyk, E. Eikefjord, J. Rørvik, A. Lundervold","doi":"10.23919/SPA.2018.8563339","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563339","url":null,"abstract":"Magnetic resonance (MR) simulation is one of the possible approaches to test and develop new imaging protocols. It can assist in fast, on-demand verification of various hypotheses concerning the impact of different physical and/or technical factors on image appearance. In this paper, we perform numerical simulation of dynamic contrast-enhanced MR imaging. In particular, we present the implementation of the so-called balanced steady state free precession sequence and show its application in the synthesis of DCE-MR images mimicking perfusion-weighted examinations of the kidney. To this end, we designed a simplified digital phantom of renal parenchyma comprising of kidney cortex and medulla. The phantom was constructed based on manual segmentation of a real high-resolution CT image of the abdomen. The contrast agent kinetics was incorporated into the model by assigning time-varying $T_{1}$ relaxation time to the kidney tissue segments. The relevant T1 time courses were determined based on analysis of real DCE-MR studies. Eventually, the practical aspects of the designed simulator are illustrated in an example application, where selected image-derived perfusion characteristics are referred to physiological parameters of the kidney.","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":"116243890","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

Simulated Local Deformation & Focal Length Optimisation For Improved Template-Based 3D Reconstruction of Non-Rigid Objects 基于改进模板的非刚性物体三维重建的模拟局部变形和焦距优化

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563402

Michał Bednarek, K. Walas

引用次数: 0

Hand Gesture Recognition by Using sEMG Signals for Human Machine Interaction Applications 基于表面肌电信号的人机交互手势识别

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563394

Fatih Serdar Sayin, Sertan Ozen, U. Baspinar

{"title":"Hand Gesture Recognition by Using sEMG Signals for Human Machine Interaction Applications","authors":"Fatih Serdar Sayin, Sertan Ozen, U. Baspinar","doi":"10.23919/SPA.2018.8563394","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563394","url":null,"abstract":"Cyber physical systems are gaining more place in daily life so interaction with the machines are increasing. Hand gestures are one of the tools for interaction with the machines and human - machines interfaces. Image processing, sensor based and sEMG based methods are the most popular for hand gesture recognition. sEMG based hand gesture recognition is chosen especially for graphical controller, hand rehabilitation software development and manipulation of robotic devices etc. In this study, classification of 5 hand motion, which are hand open, hand close, cylindrical grasp, Lateral pinch(key grasp) and index finger opening, have been realized. As a classifier, Artificial Neural Network(ANN) is used. The Data used for training and validation recorded from five subjects by using MYO® armband. Mean absolute value, slope sign change, waveform length, Willison amplitude and mean frequency features are used for classification. Classification performances were evaluated for all five subject together and each subject separately. In the study, we achieved 88.4% mean classification rate by using five subject's recordings.","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":"127380395","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: 13

Selection and tests of lossless and lossy video codecs for advanced driver-assistance systems 高级驾驶员辅助系统无损和有损视频编解码器的选择和测试

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563427

P. Pawlowski, Karol Piniarski, A. Dabrowski

引用次数: 1

Low-level audio descriptors-based analysis of music mixes from different Digital Audio Workstations – case study 来自不同数字音频工作站的基于低级音频描述符的音乐混合分析-案例研究

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563428

Damian Koszewski, B. Kostek

引用次数: 1

Automatic 3D segmentation of MRI data for detection of head and neck cancerous lymph nodes 用于头颈部癌性淋巴结检测的MRI数据自动三维分割

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563420

Baixiang Zhao, J. Soraghan, G. D. Caterina, L. Petropoulakis, D. Grose, T. Doshi

引用次数: 2

Contemporary technologies and techniques for processing of human eye images 人眼图像处理的现代技术和技术

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563426

A. Dabrowski

引用次数: 1

CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images 基于crf的动态增强MR图像药代动力学曲线聚类研究

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) Pub Date : 2018-09-01 DOI: 10.23919/SPA.2018.8563392

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

{"title":"CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images","authors":"Jakub Jurek, Mateusz Pelesz, Are Losnegård, L. Reisæter, A. Wojciechowski, A. Klepaczko, O. Halvorsen, C. Beisland, M. Kociński, A. Materka, J. Rørvik, A. Lundervold","doi":"10.23919/SPA.2018.8563392","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563392","url":null,"abstract":"Traditionally, analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE MRI) requires pharmacokinetic modelling to derive quantitative physiological parameters of the tissue. Modelling, however, is a complex task and many competing models of contrast agent kinetics and tissue structure were proposed. Alternatively, raw DCE data could be analysed to find correlation with pathology in the tissue or other desired effects, for example by clustering. In this paper, we propose a new method for DCE MRI timeseries clustering. We model the data space as a Conditional Random Field (CRF) and optimize the objective function in order to find cluster labels for all timeseries. The method is unsupervised and fully automatic. We also propose a strategy to speed up the clustering process using Support Vector Machines. We demonstrate the utility of our method on two distinct problems: prostate cancer localization and healthy kidney compartment segmentation.","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":"117347829","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