Medical image segmentation is a problem of fundamental importance in medical imageprocessing. The accurate segmentation of a medical image can provide important information for the diagnosis and treatment of many dis...
详细信息
ISBN:
(数字)9781728147437
ISBN:
(纸本)9781728147444
Medical image segmentation is a problem of fundamental importance in medical imageprocessing. The accurate segmentation of a medical image can provide important information for the diagnosis and treatment of many diseases. Since a medical image often contains noises and the objects in it are inherently complex in general, methods that can accurately segment an arbitrary medical image are still unavailable. In this paper, a new approach that combines convolutional operators and an adaptive Hidden Markov Model is developed for the segmentation of medical images. Specifically, the features associated with each pixel in a medical image are obtained with a set of convolutional operators. The semantic and spatial correlations among pixels in the image are then progressively captured by an adaptive Hidden Markov Model. The labels of the pixels can be efficiently obtained with a dynamic programming algorithm in linear time. Our experimental results show that this approach can achieve segmentation results with improved accuracy on a set of brain medical images.
statistical modeling of synthetic aperture radar (SAR) data is a crucial step in SAR imageprocessing. In this context, the generalized Gamma (GGamma) distribution, which generalizes many common distributions, has bee...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
statistical modeling of synthetic aperture radar (SAR) data is a crucial step in SAR imageprocessing. In this context, the generalized Gamma (GGamma) distribution, which generalizes many common distributions, has been recognized as a valid model. Parameter estimation remains, however, a challenging step that conditions the quality of model fitting to data and thus, of the required processing. In this paper, we propose a novel parameter estimation method for GGamma distribution in the log-domain, named as the maximum likelihood and logarithmic cumulants (ML-LC) method. The ML-LC method constructs a novel scale-independent shape parameter estimation in the log-domain based on the Mellin transform and maximum likelihood estimation, and proposes a feasible solution technique for the shape parameter based on the multistart local search (MLS), gradient descent (GD) and bisection methods. To assess the performance of our estimation method, we perform the goodness-of-fit test on SAR data. In addition, we apply the ML-LC method in some SAR data processing tasks covering image segmentation and classification. The results obtained confirm the interest of the proposed ML-LC method.
This work aims to increase the reliability of dendritic crystallogram's images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classi...
详细信息
ISBN:
(纸本)9781538678534
This work aims to increase the reliability of dendritic crystallogram's images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classification through an improved description of de dendritic structures' features. In this paper, we use the parameters of the mathematical model describing objects with dendritic structure. We developed a technology of parameters identification from a model image of dendritic structures, that was then implemented through the use of geometric and statistical features, together with a nearest neighbor classification algorithm.
The proceedings contain 94 papers. The topics discussed include: stochastic network optimization of data dissemination for multi-hop routing in VANETs;an improved adaptive sparse channel estimation method for next gen...
ISBN:
(纸本)9781538636244
The proceedings contain 94 papers. The topics discussed include: stochastic network optimization of data dissemination for multi-hop routing in VANETs;an improved adaptive sparse channel estimation method for next generation wireless broadband;music perception analysis on hearing impaired listeners;dual-band bandpass filter using complementary split ring resonators for X-band applications;multi-modal humanoid robot;fusion methods for hyperspectral image and LIDAR data at pixel-level;and accelerated feature descriptor matching in images using threshold accepting.
In this paper, a novel enhancement algorithm for low-light images captured under low illumination conditions is proposed. More concretely, we design a method firstly to synthesize low-light images as training datasets...
详细信息
ISBN:
(数字)9781510623118
ISBN:
(纸本)9781510623118
In this paper, a novel enhancement algorithm for low-light images captured under low illumination conditions is proposed. More concretely, we design a method firstly to synthesize low-light images as training datasets. Then pre-clustering is conducted to separate training data into several groups by a coupled Gaussian mixture model. For each group, we adopt a coupled dictionary learning approach to train the low-light and normal-light dictionary pair jointly, and the statistical dependency of the sparsity coefficients is captured via Extreme Learning Machine simultaneously. Besides, we use a multi-phase dictionary learning strategy to enhance the robustness of our method. Experimental results show that proposed method is superior to existing methods.
This book contains invited lecturers and full papers presented at VIPimage 2011 - III ECCOMAS Thematic conference on Computational Vision and Medical imageprocessing (Olho, Algarve, Portugal, 12-14 October 2011). Int...
ISBN:
(纸本)9781138112544
This book contains invited lecturers and full papers presented at VIPimage 2011 - III ECCOMAS Thematic conference on Computational Vision and Medical imageprocessing (Olho, Algarve, Portugal, 12-14 October 2011). International contributions from 16 countries provide a comprehensive coverage of the current state-of-the-art in:imageprocessing and Analysis;Tracking and Analyze Objects in images;Segmentation of Objects in images;3D Vision;Signal processing;Data Interpolation, Registration, Acquisition and Compression;Objects Simulation;Medical Imaging;Virtual Reality;Software Development for imageprocessing and Analysis;Computer Aided Diagnosis, Surgery, Therapy and Treatment;Computational Bioimaging and Visualization;Telemedicine Systems and their Applications. Related techniques also covered in this book include the level set method, finite element method, modal analyses, stochasticmethods, principal and independent components analyses and distribution models. Computational Vision and Medical imageprocessing - VIPimage 2011 will be useful to academics, researchers and professionals in Computational Vision (imageprocessing and Analysis), Computer Sciences, Computational Mechanics and Medicine.
image segmentation technology is one of the important topics in the field of digital imageprocessing. However, the existing image segmentation technology does not have a uniform standard, and the traditional image se...
详细信息
ISBN:
(纸本)9781538652145
image segmentation technology is one of the important topics in the field of digital imageprocessing. However, the existing image segmentation technology does not have a uniform standard, and the traditional image segmentation technology is only suitable for some fixed situations. Therefore, the image segmentation technology on new theories and new methods deserves further research and development. The SVM algorithm for image segmentation, a variety of image features can be used to get a better segmentation results. So, this paper based on the theory of support vector machines, introduces the basic idea of SVM in detail, and the current state of image segmentation and the development trend of image segmentation are described in detail. Finally, the necessity of introducing statistical learning into image segmentation and the possibility of introducing SVM into image segmentation are studied and analyzed in depth. The results show that support vector machine can well segment the image target.
In image forensics, JPEG compression history estimation problem consists of three stages: JPEG compression detection, color model identification and subsampling, and estimation of quantization steps. Although, it is w...
详细信息
The proceedings contain 11 papers. The special focus in this conference is on Biomedical image Registration. The topics include: Local image registration uncertainty estimation using polynomial chaos expansions;statis...
ISBN:
(纸本)9783319922577
The proceedings contain 11 papers. The special focus in this conference is on Biomedical image Registration. The topics include: Local image registration uncertainty estimation using polynomial chaos expansions;statistical motion mask and sliding registration;adaptive graph diffusion regularisation for discontinuity preserving image registration;fast groupwise 4D deformable image registration for irregular breathing motion estimation;a novel similarity measure for image sequences;Semi-automated processing of real-time CMR scans for left ventricle segmentation;averaged stochastic optimization for medical image registration based on variance reduction;Registration evaluation by de-enhancing CT images;evaluation of multi-metric registration for online adaptive proton therapy of prostate cancer.
The paper discusses the preconditions of the methodology development of diagnosis system for assessing dynamic impact of the rolling stock on the basis of processing and analysis of data obtained in operation on the r...
详细信息
The paper discusses the preconditions of the methodology development of diagnosis system for assessing dynamic impact of the rolling stock on the basis of processing and analysis of data obtained in operation on the results of measurement of parameters that characterize dynamic vibration processes of the mechanical system of "rolling stock track". On the basis of usage of the processingmethods of time series and stochastic processes there has been established the relationship between these dynamic processes and wheel defects, and designed experimental data processing algorithms, which in the future will be an integral part of the intellectual systems of decision making when assessing the impact level of the rolling stock on the track. The article presents some results of the experimental and theoretical research of the rail accelerations data, which have been registered during passing of the train. The computational algorithm of the specialized pre-processing of the multidimensional signal recorded by this system is described. The advantage of this algorithm is that it does not require additional information about the train speed on the section equipped with the monitoring system, about the number of wheel pairs of locomotives and train cars, and about the distances between the wheel pairs. Based on the processing results of accelerations of the rails there have been set the parameters of increase in all statistical indicators with increase in the train speed. It is noted that the level of indicators for accelerations of the rails in the vertical direction is twice as high as the corresponding parameters in the horizontal direction.
暂无评论