International Journal of Biomedical Imaging最新文献_第9页

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

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":null,"pages":null},"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

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

Saliency-Based Bleeding Localization for Wireless Capsule Endoscopy Diagnosis. 基于显著性的无线胶囊内窥镜出血定位诊断。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-11-28 DOI: 10.1155/2017/8147632

Hongda Chen, Shaoze Wang, Yong Ding, Dahong Qian

引用次数: 4

Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images. 基于稀疏码本模型的多相医学图像局部结构检索。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-02-13 DOI: 10.1155/2017/1413297

Jian Wang, Xian-Hua Han, Yingying Xu, Lanfen Lin, Hongjie Hu, Chongwu Jin, Yen-Wei Chen

{"title":"Sparse Codebook Model of Local Structures for Retrieval of Focal Liver Lesions Using Multiphase Medical Images.","authors":"Jian Wang, Xian-Hua Han, Yingying Xu, Lanfen Lin, Hongjie Hu, Chongwu Jin, Yen-Wei Chen","doi":"10.1155/2017/1413297","DOIUrl":"https://doi.org/10.1155/2017/1413297","url":null,"abstract":"Characterization and individual trait analysis of the focal liver lesions (FLL) is a challenging task in medical image processing and clinical site. The character analysis of a unconfirmed FLL case would be expected to benefit greatly from the accumulated FLL cases with experts' analysis, which can be achieved by content-based medical image retrieval (CBMIR). CBMIR mainly includes discriminated feature extraction and similarity calculation procedures. Bag-of-Visual-Words (BoVW) (codebook-based model) has been proven to be effective for different classification and retrieval tasks. This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: (1) instead of SIFT, we exploit the local patch (structure) as the local descriptor, which can retain all detailed information and is more suitable for the fine-grained medical image applications; (2) in order to more accurately approximate any local descriptor in coding procedure, the sparse coding method, instead of K-means algorithm, is employed for codebook learning and coded vector calculation; (3) we evaluate retrieval performance of focal liver lesions (FLL) using multiphase computed tomography (CT) scans, in which the proposed codebook model is separately learned for each phase. The effectiveness of the proposed method is confirmed by our experiments on FLL retrieval.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/1413297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34812206","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: 26

A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode. 钛合金电极多任务电阻抗层析成像系统。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-10-31 DOI: 10.1155/2017/3589324

Abdalla Salama, Amin Malekmohammadi, Shahram Mohanna, Rajprasad Rajkumar

引用次数: 2

Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images. 可变形图像配准对胸部ct图像三维时间相减的评价。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-10-12 DOI: 10.1155/2017/3457189

Ping Yan, Yoshie Kodera, Kazuhiro Shimamoto

{"title":"Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images.","authors":"Ping Yan, Yoshie Kodera, Kazuhiro Shimamoto","doi":"10.1155/2017/3457189","DOIUrl":"https://doi.org/10.1155/2017/3457189","url":null,"abstract":"Purpose: To perform lung image registration for reducing misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images, in order to enhance temporal changes in lung lesions and evaluate these changes after deformable image registration (DIR).Methods: In 10 cases, mutual information (MI) lung mask affine mapping combined with cross-correlation (CC) lung diffeomorphic mapping was used to implement lung volume registration. With advanced normalization tools (ANTs), we used greedy symmetric normalization (greedy SyN) as a transformation model, which involved MI-CC-SyN implementation. The resulting displacement fields were applied to warp the previous (moving) image, which was subsequently subtracted from the current (fixed) image to obtain the lung subtraction image.Results: The average minimum and maximum log-Jacobians were 0.31 and 3.74, respectively. When considering 3D landmark distance, the root-mean-square error changed from an average of 20.82 mm for Pfixed to Pmoving to 0.5 mm for Pwarped to Pfixed. Clear shadows were observed as enhanced lung nodules and lesions in subtraction images. The lesion shadows showed lesion shrinkage changes over time. Lesion tissue morphology was maintained after DIR.Conclusions: DIR (greedy SyN) effectively and accurately enhanced temporal changes in chest CT images and decreased misregistration artifacts in temporal subtraction images.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/3457189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35628394","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: 3

Future of the Renal Biopsy: Time to Change the Conventional Modality Using Nanotechnology. 肾活检的未来：利用纳米技术改变传统检查方式的时候到了。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-02-19 DOI: 10.1155/2017/6141734

Hamid Tayebi Khosroshahi, Behzad Abedi, Sabalan Daneshvar, Yashar Sarbaz, Abolhassan Shakeri Bavil

引用次数: 0

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. 基于MRI的脑肿瘤检测图像分析及生物启发BWT和SVM特征提取。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-03-06 DOI: 10.1155/2017/9749108

Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, Har Pal Thethi

{"title":"Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM.","authors":"Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, Har Pal Thethi","doi":"10.1155/2017/9749108","DOIUrl":"https://doi.org/10.1155/2017/9749108","url":null,"abstract":"The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/9749108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34877145","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: 446

Corrigendum to "Automatic Characterization of the Physiological Condition of the Carotid Artery in 2D Ultrasound Image Sequences Using Spatiotemporal and Spatiospectral 2D Maps". “使用时空和空间光谱二维地图在二维超声图像序列中自动表征颈动脉生理状况”的勘误表。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-06-28 DOI: 10.1155/2017/4237858

Hamed Hamid Muhammed, Jimmy C Azar

引用次数: 0