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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Retinal vessel landmark detection using deep learning and hessian matrix

Retinal vessel landmark detection using deep learning and he...

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congress on image and signal processing, CISP

作者： TingBing Fang Rou Su LinPei Xie QiWei Gu QiaoLiang Li Ping Liang TianFu Wang Department of Ophthalmology Shenzhen University Shenzhen China Department of Biomedical Engineering Shenzhen University Shenzhen China Shenzhen University Shenzhen Guangdong CN

The purpose of retinal image registration is to establish the coherent correspondences between the multi-model retinal image for applying into the ophthalmological surgery. Vessel landmarks detection in retinal image is the vital step in the retinal image registration. In this paper, a novel approach is proposed, firstly, a deep learning technology is used to vessel segmentation to generate the probability map of the retinal image, which is more reliable for optimizing the feature detection in retinal image. Secondly, we detect the landmarks using the multi-scale Hessian response on the probability map of the retinal image. Compared to the traditional methods, the results show that our method enable a majority of the bifurcation points, crossover points and curvature extreme points to be detected out simultaneously. Moreover, the impact of image noise and pathology can be reduced significantly.

关键词： Retina Machine learning image segmentation Feature extraction image color analysis Neural networks image registration

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Fundus imagemosaic based on the SIFT feature

Fundus imagemosaic based on the SIFT feature

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congress on image and signal processing, CISP

作者： Qiaoliang Li Qian Zhang Linpei Xie Suwen Qi Ping Liang Huisheng Zhang Tianfu Wang Department of Biomedical Engineering Shenzhen University Shenzhen

ISBN: (纸本)9781467390996

Fundus image mosaic can expand the visual field of view and provide auxiliary diagnosis for fundus diseases. Due to the low contrast and uneven grayscale of fundus images, traditional methods based on gray characteristic are no longer applicable. We propose a fundus image mosaic algorithm based on Scale-invariant feature transform (SIFT), along with a quantitative evaluation criterion. We detect feature points in Gaussian scale space and generate feature descriptors with scale invariance for all feature points. The matching feature point pairs are obtained by evaluating the similarity of their feature descriptors and outliers are eliminated using Random sample consensus (RANSAC) algorithm employed the projective transformation model. After that, the transformation matrix can be calculated based on the obtained matching point pairs. Finally, the multi-view fundus images are fused based on the transformation matrix. To the best of our knowledge, there is no quantitative evaluation reported for fundus image mosaic. Here we propose a criterion based on manual marker to evaluate the mosaic results. The experimental results show that the average accuracy is 2.27±0.5 pixels. Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases.

关键词： Feature extraction Retina image fusion Gray-scale image registration Algorithm design and analysis biomedical imaging

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Acute ischemic stroke detection using wavelet based fusion of CT and MRI images

Acute ischemic stroke detection using wavelet based fusion o...

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international Conference on Advances in Computing, Communications and informatics (ICACCI)

作者： Praveen R Mirajkar Kishan Ashok Bhagwat ArunVikas Singh Ashalatha M E PES University Bangalore Karnataka IN Dept. of Radiology SS Institute of Medical Science Davangere Karnataka India Dept. of Biomedical Engineering Bapuji Institute of Engineering & Technology Davangere Karnataka India

ISBN: (纸本)9781479987931

Ischemic stroke is a condition which causes death of brain cells due to lack of blood supply and hence stops normal brain functioning. Due to finite academic studies of ischemic stroke detection, the success rate to detect stroke initial period is low using only CT image. Fusion of CT and DW-MRI images creates a composite image which provides more information than any of the input signals provided by a single modality alone. Diffusion Weighted MRI is widely used for the detection of acute ischemic stroke compared to CT. In this paper, image of both modalities i.e. CT and DW-MRI are used. The proposed algorithm consists of four phases. Pre-processing of both images is the first phase. Finding equivalent CT image of an input pathological MRI image for image fusion is done in second phase. Third phase includes image registration and fusion of CT and MRI image. Segmentation of stroke lesion from fused image is performed in fourth phase. The performance evaluation of fusion is computed using Peak signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).

关键词： Computed tomography Magnetic resonance imaging Wavelet transforms biomedical imaging Brain image fusion

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A supervised method using convolutional neural networks for retinal vessel delineation

A supervised method using convolutional neural networks for ...

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congress on image and signal processing, CISP

作者： Qiaoliang Li Linpei Xie Qian Zhang Suwen Qi Ping Liang Huisheng Zhang Tianfu Wang Department of Biomedical Engineering Shenzhen University Shenzhen Shenzhen University Shenzhen Guangdong CN

Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application. Several methods have been proposed in the past decades. Here we will present a new supervised method for retinal vessel segmentation. The method is designed to explore the complex relationship between retinal images and their corresponding vessel label maps. Specifically, in order to build a model describing the direct transformation from retinal image to vessel map, we introduce a deep convolutional neural network (abbreviation as CNN), which has strong enough induction ability. For the purpose of constructing the whole vessel probability map, we also design a synthesis method. Our method shows better performance on DRIVE dataset than state-of-the-art of reported approaches in the light of sensitivity (abbreviation as Se), specificity (abbreviation as Sp) and accuracy (abbreviation as Acc). Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases. Meanwhile, the method may offer a novel, general computing framework for segmentation in other fields.

关键词： image segmentation Training Retinal vessels Measurement Neural networks Feature extraction

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Blind Components processing A Novel Approach to Array signal processing - A Research Orientation

Blind Components Processing A Novel Approach to Array Signal...

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international Conference on Intelligent informatics and biomedical Sciences

作者： Mahdi Khosravy Neeraj Gupta Ninoslav Marina Mohammad Reza Asharif Faramarz Asharif Ishwar K. Sethi University of Information Science and Technology Faculty of Engineering University of the Ryukyus Department of Computer Science and Engineering Oakland University in Rochester

ISBN: (纸本)9781479985630

Blind Components processing (BCP), a novel ap- proach in processing of data (signal, image, etc.) components, is introduced as well some applications to information communi- cations technology (ICT) are proposed. The newly introduced BCP is with capability of deployment orientation in a wider range of applications. The fundamental of BCP is based on Blind Source Separation (BSS), a methodology which searches for unknown sources of mixtures without a prior knowledge of either the sources or the mixing process. Most of the natural, biomedical as well as industrial observed signals are mixtures of different components while the components and the way they mixed are unknown. If we decompose the signal into its components by BSS, then we can process the components separately without interfering the other components signal/data. Such internal access to signal components leads to extraction of plenty of information as well more efficient processing compared to normal signal processing wherein all the structure of the signal is gone under processing and modification. This manuscript besides the introducing BCP, proposes a practical applications of BCP with technical merit for harmonic noise cancellation as well stock pricing model.

关键词： Blind source separation Mixing and demixing process EEG Stockpricing model

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Optimized nonlinear conjugate gradient algorithm for MR imaging reconstruction using compressed sensing 7

Optimized nonlinear conjugate gradient algorithm for MR imag...

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2014 7th international Conference on biomedical engineering and informatics, BMEI 2014

作者： Zhang, Guishan Chen, Yaowen Xiao, Gang Shen, Zhiwei Wu, Renhua Engineering College Shantou University Shantou China Math and Applied Mathematical Hanshan Normal University Chaozhou China Department of Medical Imaging 2nd Affiliated Hospital Medical College Shantou University Shantou China

ISBN: (纸本)9781479958382

Compressed sensing (CS) is an innovative theory of signal acquisition and processing based on the areas of applied mathematics. CS-based MRI exploits the sparsity of an image in an appropriate transform domain to reconstruct images from incoherently under-sampled k-space data. However, it has proven that CS-MRI suffers sharply loss of low-contrast image features with increasing reduction factors. In this work, we explored an optimized nonlinear conjugate gradient (NLCG) procedure aiming to improve peak signal to noise ratio (PSNR) of sub-sampled MRI liver T2 map and shorten the scan time markedly. Data processing and analysis were being done by the software of Matlab. Our findings indicate that using the proposed algorithm, at least 60% of the k-space data measurements necessitates for recovery. This study demonstrated the feasibility of the proposed CS approach to accelerate MRI T2 map. Further studies are needed to design the clinical sequence with sparse acquisition strategy which may be a developing technique with clinical value. © 2014 IEEE.

关键词： Compressed sensing

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Rebuilding IVUS images from raw data of the rf signal exported by IVUS equipment

Rebuilding IVUS images from raw data of the rf signal export...

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2014 international Conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2014, at WORLDCOMP 2014

作者： Granero, Marco Aurélio Gutierrez, Marco Antônio Costa, Eduardo Tavares Department of Biomedical Engineering DEB/FEEC/UNICAMP Campinas Brazil Division of Informatics Heart Institute - HCFMUSP São Paulo Brazil Federal Institute of Education Science and Technology S. Paulo - IFSP São Paulo Brazil

ISBN: (纸本)1601322801

The study of composition and classification of atherosclerotic plaque has been a very active research field, both in cardiology and image processing. Intravascular ultrasound (IVUS) is an effective tool, which can insights about the crosssection of blood vessels, with sufficient accuracy to allow an accurate assessment of CT slices. This enables information about blood vessel structures to be determined. During an IVUS medical examination, physicians subjectively adjust a set of parameters to improve the visualization of a Region Of Interest (ROI) and produce corresponding images in Digital Imaging and Communications (DICOM) format, for later analysis and study. DICOM is appropriate for storage, transportation and access, but limits subsequent changes to image parameters, such as contrast or brightness. This makes comparison across patient populations difficult and restricts image processing operations. This paper details an alternative to using DICOM, which is to rebuild IVUS images from raw radiofrequency signal (RF) data. The main advantage of this process is the independence of the acquisition parameters adjusted during the exam. This advantage makes possible the comparison between exams and can be used to monitor the evolution of cardiovascular disease. Beyond this, once the reconstructed images and the RF signal are stored, operations relating to texture and spectral analysis can be carried out and automatic classifiers employed. From a clinical point of view these reconstructed images share the same characteristics as DICOM images with an advantage that the former have a higher contrast than the latter, allowing deeper regions to be seen. © 2014 CSREA Press.

关键词： Blood vessels

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Optimized nonlinear conjugate gradient algorithm for MR imaging reconstruction using compressed sensing

Optimized nonlinear conjugate gradient algorithm for MR imag...

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international Conference on biomedical engineering and informatics (BMEI)

作者： Guishan Zhang Yaowen Chen Gang Xiao Zhiwei Shen Renhua Wu Shantou University Shantou Guangdong CN Hanshan Normal University Chaozhou Guangdong CN

ISBN: (纸本)9781479958399

关键词： image reconstruction Magnetic resonance imaging PSNR Compressed sensing Transforms Liver

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An improved Criminisi algorithm based on a new priority function and updating confidence

An improved Criminisi algorithm based on a new priority func...

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international Conference on biomedical engineering and informatics (BMEI)

作者： Ajian Nan Xiaoqiang Xi School of Science Xi'an University of Posts and Telecommunications Xi'an China Institute of Internet of Things and IT-based Industrialization Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781479958399

In the Criminisi algorithm, the unreliability filling order will influence the inpainting quality. Our objective is to discover a method to mend this defect. An improved Criminisi algorithm was proposed by changing the priority and the confidence. We use (1) addition instead of multiplication in the computing of the priority function and the golden section as the weight ratio; and (2) introduce the logistic equation in the confidence update to avoid its rapid decay. Experiments show that our improved algorithm can obtain a better restore effect than that of the original Criminisi algorithm.

关键词： Mathematical model image restoration PSNR signal processing algorithms Maintenance engineering Equations Filling

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A New Approach to Reversible Watermarking

A New Approach to Reversible Watermarking

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international Conference on Intelligent Information Hiding and Multimedia signal processing (IIHMSP)

作者： Toshiki Ito Ryo Sugimura Hyunho Kang Keiichi Iwamura Kitahiro Kaneda Isao Echizen Department of Electrical Engineering Tokyo University of Science Tokyo Japan National Institute of Informatics Tokyo Japan

A general digital watermarking application could not prevent the distortion of some sensitive content, like medical or military images. Since such changing of medical images is not acceptable, a reversible watermarking technique must be used to completely restore the original image after the watermark information has been extracted. The use of such a technique also enables the embedding of multiple watermarks. This paper survey recent watermarking methods and identifies research trends. A combination of two particular methods is shown to achieve the highest embedding capacity and best image quality. The reason for this is discussed and proposed as a new method.

关键词： Watermarking image quality Conferences PSNR image restoration biomedical imaging

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