International Journal of Biomedical Imaging最新文献_第7页

Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms. 脑中线偏移测量及其自动化:技术和算法综述。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-04-12 eCollection Date: 2018-01-01 DOI: 10.1155/2018/4303161

Chun-Chih Liao, Ya-Fang Chen, Furen Xiao

{"title":"Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms.","authors":"Chun-Chih Liao, Ya-Fang Chen, Furen Xiao","doi":"10.1155/2018/4303161","DOIUrl":"10.1155/2018/4303161","url":null,"abstract":"Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"4303161"},"PeriodicalIF":7.6,"publicationDate":"2018-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/4303161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36177714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

Cryo-Imaging and Software Platform for Analysis of Molecular MR Imaging of Micrometastases. 冷冻成像及微转移瘤分子磁共振成像分析软件平台。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-04-01 eCollection Date: 2018-01-01 DOI: 10.1155/2018/9780349

Mohammed Q Qutaish, Zhuxian Zhou, David Prabhu, Yiqiao Liu, Mallory R Busso, Donna Izadnegahdar, Madhusudhana Gargesha, Hong Lu, Zheng-Rong Lu, David L Wilson

{"title":"Cryo-Imaging and Software Platform for Analysis of Molecular MR Imaging of Micrometastases.","authors":"Mohammed Q Qutaish, Zhuxian Zhou, David Prabhu, Yiqiao Liu, Mallory R Busso, Donna Izadnegahdar, Madhusudhana Gargesha, Hong Lu, Zheng-Rong Lu, David L Wilson","doi":"10.1155/2018/9780349","DOIUrl":"https://doi.org/10.1155/2018/9780349","url":null,"abstract":"We created and evaluated a preclinical, multimodality imaging, and software platform to assess molecular imaging of small metastases. This included experimental methods (e.g., GFP-labeled tumor and high resolution multispectral cryo-imaging), nonrigid image registration, and interactive visualization of imaging agent targeting. We describe technological details earlier applied to GFP-labeled metastatic tumor targeting by molecular MR (CREKA-Gd) and red fluorescent (CREKA-Cy5) imaging agents. Optimized nonrigid cryo-MRI registration enabled nonambiguous association of MR signals to GFP tumors. Interactive visualization of out-of-RAM volumetric image data allowed one to zoom to a GFP-labeled micrometastasis, determine its anatomical location from color cryo-images, and establish the presence/absence of targeted CREKA-Gd and CREKA-Cy5. In a mouse with >160 GFP-labeled tumors, we determined that in the MR images every tumor in the lung >0.3 mm2 had visible signal and that some metastases as small as 0.1 mm2 were also visible. More tumors were visible in CREKA-Cy5 than in CREKA-Gd MRI. Tape transfer method and nonrigid registration allowed accurate (<11 μm error) registration of whole mouse histology to corresponding cryo-images. Histology showed inflammation and necrotic regions not labeled by imaging agents. This mouse-to-cells multiscale and multimodality platform should uniquely enable more informative and accurate studies of metastatic cancer imaging and therapy.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"9780349"},"PeriodicalIF":7.6,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/9780349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36134918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Classification of Alzheimer's and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET. 从语义分割的 PET 图像对阿尔茨海默氏症和 MCI 患者进行分类：AV45 和 FDG-PET 的比较。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-03-15 eCollection Date: 2018-01-01 DOI: 10.1155/2018/1247430

Seyed Hossein Nozadi, Samuel Kadoury, The Alzheimer's Disease Neuroimaging Initiative

{"title":"Classification of Alzheimer's and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET.","authors":"Seyed Hossein Nozadi, Samuel Kadoury, The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1155/2018/1247430","DOIUrl":"10.1155/2018/1247430","url":null,"abstract":"Early identification of dementia in the early or late stages of mild cognitive impairment (MCI) is crucial for a timely diagnosis and slowing down the progression of Alzheimer's disease (AD). Positron emission tomography (PET) is considered a highly powerful diagnostic biomarker, but few approaches investigated the efficacy of focusing on localized PET-active areas for classification purposes. In this work, we propose a pipeline using learned features from semantically labelled PET images to perform group classification. A deformable multimodal PET-MRI registration method is employed to fuse an annotated MNI template to each patient-specific PET scan, generating a fully labelled volume from which 10 common regions of interest used for AD diagnosis are extracted. The method was evaluated on 660 subjects from the ADNI database, yielding a classification accuracy of 91.2% for AD versus NC when using random forests combining features from cross-sectional and follow-up exams. A considerable improvement in the early versus late MCI classification accuracy was achieved using FDG-PET compared to the AV-45 compound, yielding a 72.5% rate. The pipeline demonstrates the potential of exploiting longitudinal multiregion PET features to improve cognitive assessment.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"1247430"},"PeriodicalIF":7.6,"publicationDate":"2018-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36077527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of Visual Information Presentation for Visual Prosthesis. 视觉义肢视觉信息呈现的优化。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-03-14 eCollection Date: 2018-01-01 DOI: 10.1155/2018/3198342

Fei Guo, Yuan Yang, Yong Gao

{"title":"Optimization of Visual Information Presentation for Visual Prosthesis.","authors":"Fei Guo, Yuan Yang, Yong Gao","doi":"10.1155/2018/3198342","DOIUrl":"https://doi.org/10.1155/2018/3198342","url":null,"abstract":"Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"3198342"},"PeriodicalIF":7.6,"publicationDate":"2018-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/3198342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36074539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System. 微波成像系统中基于EBG的微带贴片天线散射参数检测脑肿瘤。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-02-12 eCollection Date: 2018-01-01 DOI: 10.1155/2018/8241438

Reefat Inum, Md Masud Rana, Kamrun Nahar Shushama, Md Anwarul Quader

{"title":"EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System.","authors":"Reefat Inum, Md Masud Rana, Kamrun Nahar Shushama, Md Anwarul Quader","doi":"10.1155/2018/8241438","DOIUrl":"https://doi.org/10.1155/2018/8241438","url":null,"abstract":"A microwave brain imaging system model is envisaged to detect and visualize tumor inside the human brain. A compact and efficient microstrip patch antenna is used in the imaging technique to transmit equivalent signal and receive backscattering signal from the stratified human head model. Electromagnetic band gap (EBG) structure is incorporated on the antenna ground plane to enhance the performance. Rectangular and circular EBG structures are proposed to investigate the antenna performance. Incorporation of circular EBG on the antenna ground plane provides an improvement of 22.77% in return loss, 5.84% in impedance bandwidth, and 16.53% in antenna gain with respect to the patch antenna with rectangular EBG. The simulation results obtained from CST are compared to those obtained from HFSS to validate the design. Specific absorption rate (SAR) of the modeled head tissue for the proposed antenna is determined. Different SAR values are compared with the established standard SAR limit to provide a safety regulation of the imaging system. A monostatic radar-based confocal microwave imaging algorithm is applied to generate the image of tumor inside a six-layer human head phantom model. S-parameter signals obtained from circular EBG loaded patch antenna in different scanning modes are utilized in the imaging algorithm to effectively produce a high-resolution image which reliably indicates the presence of tumor inside human brain.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"8241438"},"PeriodicalIF":7.6,"publicationDate":"2018-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/8241438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35981701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 44

Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm. 基于熵的视网膜图像视盘自动定位算法。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-02-06 eCollection Date: 2018-01-01 DOI: 10.1155/2018/2815163

Lamia AbedNoor Muhammed

{"title":"Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm.","authors":"Lamia AbedNoor Muhammed","doi":"10.1155/2018/2815163","DOIUrl":"https://doi.org/10.1155/2018/2815163","url":null,"abstract":"Examining retinal image continuously plays an important role in determining human eye health; with any variation present in this image, it may be resulting from some disease. Therefore, there is a need for computer-aided scanning for retinal image to perform this task automatically and accurately. The fundamental step in this task is identification of the retina elements; optical disk localization is the most important one in this identification. Different optical disc localization algorithms have been suggested, such as an algorithm that would be proposed in this paper. The assumption is based on the fact that optical disc area has rich information, so its entropy value is more significant in this area. The suggested algorithm has recursive steps for testing the entropy of different patches in image; sliding window technique is used to get these patches in a specific way. The results of practical work were obtained using different common data set, which achieved good accuracy in trivial computation time. Finally, this paper consists of four sections: a section for introduction containing the related works, a section for methodology and material, a section for practical work with results, and a section for conclusion.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"2815163"},"PeriodicalIF":7.6,"publicationDate":"2018-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/2815163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35924882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l₁-Norm Approximation. 使用平滑 l1-正则近似对压缩采样的自由呼吸心脏磁共振成像进行呼吸运动校正

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-01-23 eCollection Date: 2018-01-01 DOI: 10.1155/2018/7803067

Muhammad Bilal, Jawad Ali Shah, Ijaz M Qureshi, Kushsairy Kadir

{"title":"Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation.","authors":"Muhammad Bilal, Jawad Ali Shah, Ijaz M Qureshi, Kushsairy Kadir","doi":"10.1155/2018/7803067","DOIUrl":"10.1155/2018/7803067","url":null,"abstract":"Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"7803067"},"PeriodicalIF":7.6,"publicationDate":"2018-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35971299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Narrow-Energy-Width CT Based on Multivoltage X-Ray Image Decomposition. 基于多电压x射线图像分解的窄能宽CT。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-11-07 DOI: 10.1155/2017/8126019

Jiaotong Wei, Yan Han, Ping Chen

引用次数: 3

Corrigendum to "Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models". “使用高斯混合模型的磁共振成像在脑肿瘤中的自动特征提取”的更正。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-08-13 DOI: 10.1155/2017/3247974

Ahmad Chaddad, Markus Luedi, Pascal O Zinn, Rivka Colen

引用次数: 1

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features. 基于彩色小波与卷积神经网络特征融合的视频内镜胃肠道息肉自动检测系统。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-08-14 DOI: 10.1155/2017/9545920

Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman

{"title":"An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features.","authors":"Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman","doi":"10.1155/2017/9545920","DOIUrl":"https://doi.org/10.1155/2017/9545920","url":null,"abstract":"Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2017 ","pages":"9545920"},"PeriodicalIF":7.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/9545920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35396866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 83