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

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

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