检索结果-内蒙古大学图书馆

Validation of an automatic segmentation method to detect vertebral interfaces in ultrasound images

IET SCIENCE MEASUREMENT & TECHNOLOGY 2016年第1期10卷 18-27页

作者： Aventaggiato, Matteo Conversano, Francesco Pisani, Paola Casciaro, Ernesto Franchini, Roberto Lay-Ekuakille, Aime Muratore, Maurizio Casciaro, Sergio Echolight Srl Lecce Italy CNR Inst Clin Physiol Lecce Italy Univ Salento Dept Innovat Engn Lecce Italy Galateo Hosp OU Rheumatol ASL LE Lecce Italy

Aim of this study was to perform a detailed clinical validation of a new fully automatic algorithm for vertebral interface segmentation in echographic images. Abdominal echographic scans of lumbar vertebrae L1-L4 were carried out on 150 female subjects with variable age and body mass index (BMI). Acquired datasets were automatically processed by the algorithm and the accuracy of the obtained segmentations was then evaluated by three independent experienced operators. Obtained results showed a very good specificity in vertebra detection (93.3%), coupled with a reasonable sensitivity (68.1%), representing a suitable compromise between the detection of a sufficient number of vertebrae for reliable diagnoses and the limitation of the corresponding computation time. Importantly, there was only a minimum presence of false vertebrae' detected (2.8%), resulting in a very low influence on subsequent diagnostic analyses. Furthermore, the algorithm was specifically tuned to provide an improved sensitivity (up to 73.1%) with increasing patient BMI, to keep a suitable number of correctly detected vertebrae even when the acquisition was intrinsically more difficult because of the augmented thickness of abdominal soft tissues. The proposed algorithm will represent an essential added value for developing echographic methods for the diagnosis of osteoporosis on lumbar vertebrae.

关键词： bone orthopaedics biomedical ultrasonics ultrasonic imaging image segmentation medical image processing data acquisition automatic segmentation method vertebral interface segmentation ultrasound imaging echographic imaging abdominal echographic scans lumbar vertebrae L1-L4 false vertebrae detection abdominal soft tissues osteoporosis diagnosis

来源：评论

学校读者我要写书评

暂无评论

automatic segmentation method of Touching Corn Kernels in Digital Image Based on Improved Watershed Algorithm

Automatic Segmentation Method of Touching Corn Kernels in Di...

引用

2011 International Conference on New Technology of Agricultural Engineering(ICAE 2011)

作者： Quan Longzhe Engineering College of Engineering Northeast Agricultural University Harbin,china Jiang Enchen Engineering College of Engineering Northeast Agricultural University Harbin,china

in order to separate touching corn kernels in digital image,this paper made the improvement to the watershed algorithm,this improved method can resolve the problems of 'algorithm-over-segmentation'and'leakage',and realized automatic segmentation of touching corn ***,in order to get the pretreatment image,Wiener filter and mathematical morphology were adopted to reduce noises and clarify background of the image;secondly,by combining with tow edge detection operators,the boundary of touching corn kernels was determined in the image,which can serve as watershed of the algorithm;thirdly,erode transform was used to construct basin of the algorithm;at last,the algorithm of purifying image background was proposed,solving the problem of'leakage'.The results of experiment showed that the correction rate of segmentation is 94%by this automatic segmentation method.

关键词： corn kernels Touching automatic segmentation method watershed algorithm

来源：评论

学校读者我要写书评

暂无评论

Detection of Breast Abnormalities of Thermograms based on a New segmentation method

Detection of Breast Abnormalities of Thermograms based on a ...

引用

3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Ali, Mona A. S. Sayed, Gehad Ismail Gaber, Tarek Hassanien, Aboul Ella Snasel, Vaclav Silva, Lincoln F. Suez Canal Univ Fac Comp & Informat Ismailia Egypt VSB TU Ostrava IT4Innovat Ostrava Czech Republic Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Cairo Egypt VSB TU Ostrava Dept Comp Sci FEECS Ostrava Czech Republic Univ Fed Fluminense Dept Comp Sci BR-24220000 Niteroi RJ Brazil

ISBN: (纸本)9788360810668

Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

关键词： cancer feature extraction image classification image segmentation image texture infrared imaging mammography medical image processing statistical analysis support vector machines visual databases CAD system DMR-IR database ROI SVM abnormal breast detection automatic segmentation method breast abnormality detection breast cancer breast thermograms classification accuracy data acquisition protocol parameter image statistics kernel function normal breast detection precision value recall value region-of-interest statistical feature extraction texture feature extraction Accuracy Breast Feature extraction Image segmentation Protocols Support vector machines Training Support Vector Network Feature extraction image segmentation computer aided design system Thermogram Breast visual databases image texture Medical image processing Infrared imaging Image classification protocols Digital Mammography region of interest Breast Cancer

来源：评论

学校读者我要写书评

暂无评论

DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI segmentation

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第6期25卷 2018-2028页

作者： Yang, Xinyu Zhang, Yuan Lo, Benny Wu, Dongrui Liao, Hongen Zhang, Yuan-Ting Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Imperial Coll London Hamlyn Ctr London SW7 2AZ England Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn Hong Kong Peoples R China

With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

关键词： Image segmentation Magnetic resonance imaging Convolution Training Informatics Kernel Biomedical imaging Cardiac MRI Medical Image Processing automatic segmentation method Adversarial Network

来源：评论

学校读者我要写书评

暂无评论

automatic lung segmentation based on Graph Cut using a distance-constrained energy

引用

IET COMPUTER VISION 2018年第5期12卷 609-615页

作者： Oluyide, Oluwakorede M. Tapamo, Jules-Raymond Viriri, Serestina Univ KwaZulu Natal Sch Engn ZA-4041 Durban South Africa Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-4000 Durban South Africa

Lung segmentation serves to ensure that all the parts of the lungs are considered during pulmonary image analysis by isolating the lung from the surrounding anatomy in the image. Research has shown that computed tomography (CT) images greatly improves the accuracy of the diagnosis obtained by a physician for lung cancer detection. Therefore, inspired by the success of Graph Cut in image segmentation and given that manual methods of analysing CT images are tedious and time-consuming, an automatic segmentation method based on Graph Cut is proposed which makes use of a distance-constrained energy (DCE). Graph Cut produces globally optimal solutions by modelling the image data and spatial relationship among the pixels. However, several anatomical regions in the thoracic CT image have pixel intensity values similar to the lungs, leading to results where the lung tissue and all these regions are included in the segmentation result. The global energy function is, therefore, further constrained by using the distance of pixels from a coarsely segmented region of the CT image containing the lungs. The proposed method, utilising the DCE function, shows significant improvement over using the unconstrained energy function in segmenting the lungs from the CT images using Graph Cut.

关键词： image segmentation computerised tomography medical image processing image resolution lung automatic lung segmentation Graph Cut distance-constrained energy pulmonary image analysis computed tomography images automatic segmentation method DCE pixel intensity values global energy function

来源：评论

学校读者我要写书评

暂无评论

1093 automatic delineation of endo- and epicardial contours in late-enhancement cardiac MR images

引用

Journal of Cardiovascular Magnetic Resonance 2008年第1期10卷 1-3页

作者： Cybele Ciofolo Maxim Fradkin Benoit Mory Gilion Hautvast Marcel Breeuwer Philips Medical Systems Research Paris Suresnes Cedex France Philips Medical Systems Nederland B.V. Best The Netherlands

来源：评论

学校读者我要写书评

暂无评论

AN UNSUPERVISED method FOR EQUIVALENT NUMBER OF LOOKS ESTIMATION IN COMPLEX SAR SCENES

AN UNSUPERVISED METHOD FOR EQUIVALENT NUMBER OF LOOKS ESTIMA...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Dingsheng Hu Anthony P. Doulgeris Xiaolan Qiu Dept. of Phys. & Technol. Univ. of Tromso Tromso Norway

ISBN: (纸本)9781479979301

This paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties of ENL data into an automatic segmentation method, we isolate the sub-class affected least by mixture and texture and suggest taking the mean value of this class as the final ENL estimate. The proposed estimator is evaluated in the experiments performed on simulated and real data from two very different sensors. It always gives better results than the other two existing methods and possesses greater adaptability.

关键词： image segmentation remote sensing speckle synthetic aperture radar unsupervised learning ENL data ENL estimation Equivalent Number of Looks arbitrary image automatic segmentation method complex SAR scene unsupervised ENL estimator unsupervised method Data models Histograms Image segmentation Maximum likelihood estimation Robustness Synthetic aperture radar equivalent number of looks (ENL) unsupervised estimation Synthetic aperture radar image segmentation Speckle data models Unsupervised learning maximum likelihood estimation histograms MLLT1 gene remote sensing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：