International Journal of Biomedical Imaging最新文献_第6页

Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs. 回顾:胸部前后片结节分割的研究。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-10-18 eCollection Date: 2018-01-01 DOI: 10.1155/2018/9752638

S K Chaya Devi, T Satya Savithri

引用次数: 4

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm. 基于可调q -小波和萤火虫特征选择算法的癫痫自动检测方法。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-09-10 eCollection Date: 2018-01-01 DOI: 10.1155/2018/5812872

Ahmed I Sharaf, Mohamed Abu El-Soud, Ibrahim M El-Henawy

{"title":"An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm.","authors":"Ahmed I Sharaf, Mohamed Abu El-Soud, Ibrahim M El-Henawy","doi":"10.1155/2018/5812872","DOIUrl":"https://doi.org/10.1155/2018/5812872","url":null,"abstract":"Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"5812872"},"PeriodicalIF":7.6,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/5812872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36541471","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

Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting. 基于局部和全局先验正则化和频谱拟合的凸优化磁共振图像超分辨率。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-09-02 eCollection Date: 2018-01-01 DOI: 10.1155/2018/9262847

Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani

{"title":"Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting.","authors":"Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani","doi":"10.1155/2018/9262847","DOIUrl":"https://doi.org/10.1155/2018/9262847","url":null,"abstract":"Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain. However, the nature of the super-resolution is to restore high-frequency components in frequency domain rather than upsampling in signal domain. In that sense, there is a close relationship between super-resolution of an image and extrapolation of the spectrum. In this study, we propose a novel framework for super-resolution, where the high-frequency components are theoretically restored with respect to the frequency fidelities. This framework helps to introduce multiple simultaneous regularizers in both signal and frequency domains. Furthermore, we propose a new super-resolution model where frequency fidelity, low-rank (LR) prior, low total variation (TV) prior, and boundary prior are considered at once. The proposed method is formulated as a convex optimization problem which can be solved by the alternating direction method of multipliers. The proposed method is the generalized form of the multiple super-resolution methods such as TV super-resolution, LR and TV super-resolution, and the Gerchberg method. Experimental results show the utility of the proposed method comparing with some existing methods using both simulational and practical images.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"9262847"},"PeriodicalIF":7.6,"publicationDate":"2018-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/9262847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36518770","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: 4

Corrigendum to "Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography". “光子计数计算机断层扫描的多色迭代统计材料图像重建”的勘误表。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-08-09 eCollection Date: 2018-01-01 DOI: 10.1155/2018/5932653

Thomas Weidinger, Thorsten M Buzug, Thomas G Flohr, Steffen Kappler, Karl Stierstorfer

引用次数: 0

Medical Image Blind Integrity Verification with Krawtchouk Moments. 基于克rawtchouk矩的医学图像盲完整性验证。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-07-02 eCollection Date: 2018-01-01 DOI: 10.1155/2018/2572431

Xu Zhang, Xilin Liu, Yang Chen, Huazhong Shu

引用次数: 5

Estimation of the Craniectomy Surface Area by Using Postoperative Images. 应用术后图像估计颅骨切除术表面积。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-06-03 eCollection Date: 2018-01-01 DOI: 10.1155/2018/5237693

Meng-Yin Ho, Wei-Lung Tseng, Furen Xiao

{"title":"Estimation of the Craniectomy Surface Area by Using Postoperative Images.","authors":"Meng-Yin Ho, Wei-Lung Tseng, Furen Xiao","doi":"10.1155/2018/5237693","DOIUrl":"https://doi.org/10.1155/2018/5237693","url":null,"abstract":"Decompressive craniectomy (DC) is a neurosurgical procedure performed to relieve the intracranial pressure engendered by brain swelling. However, no easy and accurate method exists for determining the craniectomy surface area. In this study, we implemented and compared three methods of estimating the craniectomy surface area for evaluating the decompressive effort. We collected 118 sets of preoperative and postoperative brain computed tomography images from patients who underwent craniectomy procedures between April 2009 and April 2011. The surface area associated with each craniectomy was estimated using the marching cube and quasi-Monte Carlo methods. The surface area was also estimated using a simple AC method, in which the area is calculated by multiplying the craniectomy length (A) by its height (C). The estimated surface area ranged from 9.46 to 205.32 cm2, with a median of 134.80 cm2. The root-mean-square deviation (RMSD) between the marching cube and quasi-Monte Carlo methods was 7.53 cm2. Furthermore, the RMSD was 14.45 cm2 between the marching cube and AC methods and 12.70 cm2 between the quasi-Monte Carlo and AC methods. Paired t-tests indicated no statistically significant difference between these methods. The marching cube and quasi-Monte Carlo methods yield similar results. The results calculated using the AC method are also clinically acceptable for estimating the DC surface area. Our results can facilitate additional studies on the association of decompressive effort with the effect of craniectomy.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"5237693"},"PeriodicalIF":7.6,"publicationDate":"2018-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/5237693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36282726","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: 7

Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data. 即时反馈快速原型的gpu加速计算，操作和多维数据的可视化。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-06-03 eCollection Date: 2018-01-01 DOI: 10.1155/2018/2046269

Maximilian Malek, Christoph W Sensen

{"title":"Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data.","authors":"Maximilian Malek, Christoph W Sensen","doi":"10.1155/2018/2046269","DOIUrl":"https://doi.org/10.1155/2018/2046269","url":null,"abstract":"Objective: We have created an open-source application and framework for rapid GPU-accelerated prototyping, targeting image analysis, including volumetric images such as CT or MRI data.Methods: A visual graph editor enables the design of processing pipelines without programming. Run-time compiled compute shaders enable prototyping of complex operations in a matter of minutes.Results: GPU-acceleration increases processing the speed by at least an order of magnitude when compared to traditional multithreaded CPU-based implementations, while offering the flexibility of scripted implementations.Conclusion: Our framework enables real-time, intuition-guided accelerated algorithm and method development, supported by built-in scriptable visualization.Significance: This is, to our knowledge, the first tool for medical data analysis that provides both high performance and rapid prototyping. As such, it has the potential to act as a force multiplier for further research, enabling handling of high-resolution datasets while providing quasi-instant feedback and visualization of results.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"2046269"},"PeriodicalIF":7.6,"publicationDate":"2018-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/2046269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36282725","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

Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation. 自适应差分同构多分辨率算法及其在大变形同模态医学图像配准中的应用。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-05-16 eCollection Date: 2018-01-01 DOI: 10.1155/2018/7314612

Chang Wang, Qiongqiong Ren, Xin Qin, Yi Yu

{"title":"Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation.","authors":"Chang Wang, Qiongqiong Ren, Xin Qin, Yi Yu","doi":"10.1155/2018/7314612","DOIUrl":"https://doi.org/10.1155/2018/7314612","url":null,"abstract":"Diffeomorphic demons can guarantee smooth and reversible deformation and avoid unreasonable deformation. However, the number of iterations needs to be set manually, and this greatly influences the registration result. In order to solve this problem, we proposed adaptive diffeomorphic multiresolution demons in this paper. We used an optimized framework with nonrigid registration and diffeomorphism strategy, designed a similarity energy function based on grey value, and stopped iterations adaptively. This method was tested by synthetic image and same modality medical image. Large deformation was simulated by rotational distortion and extrusion transform, medical image registration with large deformation was performed, and quantitative analyses were conducted using the registration evaluation indexes, and the influence of different driving forces and parameters on the registration result was analyzed. The registration results of same modality medical images were compared with those obtained using active demons, additive demons, and diffeomorphic demons. Quantitative analyses showed that the proposed method's normalized cross-correlation coefficient and structural similarity were the highest and mean square error was the lowest. Medical image registration with large deformation could be performed successfully; evaluation indexes remained stable with an increase in deformation strength. The proposed method is effective and robust, and it can be applied to nonrigid registration of same modality medical images with large deformation.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"7314612"},"PeriodicalIF":7.6,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/7314612","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36210374","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: 4

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics. 结合自动分割和放射组学的胶质瘤计算机辅助分级。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-05-08 eCollection Date: 2018-01-01 DOI: 10.1155/2018/2512037

Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao

{"title":"Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.","authors":"Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao","doi":"10.1155/2018/2512037","DOIUrl":"https://doi.org/10.1155/2018/2512037","url":null,"abstract":"Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"2512037"},"PeriodicalIF":7.6,"publicationDate":"2018-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/2512037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36181830","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: 60

Corrigendum to "Recent Advances in Microwave Imaging for Breast Cancer Detection". “微波成像检测乳腺癌的最新进展”的勘误表。

IF 7.6

International Journal of Biomedical Imaging Pub Date : 2018-05-02 eCollection Date: 2018-01-01 DOI: 10.1155/2018/1657073

Sollip Kwon, Seungjun Lee

引用次数: 2