Vedic Multiplier is a key tool in rapidly growing technology especially in the immense domain of imageprocessing, Digital Signal processing, real-time signal. Multipliers are important block in digital systems and pl...
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image segmentation is a key stage in medical imageprocessingalgorithms and machine learning classifiers where identification of discriminative features are of utmost importance. In the case of skin lesions, most of ...
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ISBN:
(纸本)9789897583537
image segmentation is a key stage in medical imageprocessingalgorithms and machine learning classifiers where identification of discriminative features are of utmost importance. In the case of skin lesions, most of the existing image segmentation approaches aim at minimising some error metric between computed and ground-truth regions of interest (ROI) defined by medical experts, where ROI delineation is not always considered. This paper proposes an image segmentation method for skin lesion delineation, which expands traditional histogram and clustering-based approaches to achieve the best trade-off between both. The proposed method is capable of providing accurate details of the skin lesion borders, without deviating from the coarser borders of the available ground-truth.
In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. This procedure raises questions on how to compare the results obtained by automatic al...
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ISBN:
(纸本)9783319914763;9783319914756
In the field of medical imaging, ground truth is often gathered from groups of experts, whose outputs are generally heterogeneous. This procedure raises questions on how to compare the results obtained by automatic algorithms to multiple ground truth items. Secondarily, it raises questions on the meaning of the divergences between experts. In this work, we focus on the case of immunohistochemistry image segmentation and analysis. We propose measures to quantify the divergence in groups of ground truth images, and we observe their behaviour. These measures are based upon fusion techniques for binary images, which is a common example of non-monotone data fusion process. Our measures can be used not only in this specific field of medical imagery, but also in any task related to meta-quality evaluation for imageprocessing, e.g. ground truth validation or expert rating.
Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Technically, medical imaging mainly processes uncertain, missing, a...
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ISBN:
(纸本)9781618040114
Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Technically, medical imaging mainly processes uncertain, missing, ambiguous, complementary, inconsistent, redundant contradictory, distorted data and information has a strong structural character. As a general approach, the understanding of any image involves the matching of features extracted from the image with pre-stored models. The production of a high-level symbolic model requires the representation of knowledge about the objects to be modeled, their relationships, and how and when to use the information stored within the model. This paper reports new (semi)automated methods for the segmentation and classification of medical images using soft computing techniques (e.g. fuzzy logic, neural networks, genetic algorithms), information fusion and specific domain knowledge. Fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information ("hybridization"), as well as structural information constituted mainly by spatial relationships in biomedical imaging. Promising results show the superiority of the soft computing and knowledge-based approach over best traditional techniques in terms of segmentation errors. The classification of different anatomic structures is made by implementing rules yielded both by domain literature and by medical experts. Though the proposed methodology has been implemented and successfully used for model-driven in the domain of medical imaging, the deployed methods are generic and applicable to any structure that can be defined by expert knowledge and morphological image analysis.
The proceedings contains 125 papers from the Eight conference on Innovative Applications of Artificial Intelligence. Topics discussed include: autonomous mobile robots;motion planning;model-based reasoning and diagnos...
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The proceedings contains 125 papers from the Eight conference on Innovative Applications of Artificial Intelligence. Topics discussed include: autonomous mobile robots;motion planning;model-based reasoning and diagnosis;qualitative simulation;constraint theory;spatial and functional reasoning;natural language processing;computational semantics;tree bank grammars;speech recognition;computer vision;color imageprocessing;temporal and rule-based reasoning;Bayesian networks;learning systems;knowledge based systems;recurrent expert networks;combinatorial optimization;network algorithms;Markov process modeling;query languages;handwriting recognition and interpretation;stochastic searching;and decision processes.
Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better effic...
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Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper...
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ISBN:
(数字)9781510616400
ISBN:
(纸本)9781510616400
Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper, we propose a semi-automated method for segmentation of myocardial scar from late gadolinium enhancement magnetic resonance image (LGE-MRI) using a convolutional neural network (CNN). In contrast to image intensity-based methods, CNN based algorithms have the potential to improve the accuracy of scar segmentation through the creation of high-level features from a combination of convolutional, detection and pooling layers. Our developed algorithm was trained using 2,336,703 image patches extracted from 420 slices of five 3D LGE-MR datasets, then validated on 2,204,178 patches from a testing dataset of seven 3D LGE-MR images including 624 slices, all obtained from patients with chronic myocardial infarction. For evaluation of the algorithm, we compared the algorithm-generated segmentations to manual delineations by experts. Our CNN-based method reported an average Dice similarity coefficient (DSC), precision, and recall of 94.50 +/- 3.62%, 96.08 +/- 3.10%, and 93.96 +/- 3.75% as the accuracy of segmentation, respectively. As compared to several intensity threshold-based methods for scar segmentation, the results of our developed method have a greater agreement with manual expert segmentation.
The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledge...
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A new algorithm is proposed to obtain very high resolution time-frequency analysis of signal components with curved timefrequency supports. The proposed algorithm is based on fractional Fourier domain warping concept ...
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ISBN:
(纸本)0780370414
A new algorithm is proposed to obtain very high resolution time-frequency analysis of signal components with curved timefrequency supports. The proposed algorithm is based on fractional Fourier domain warping concept introduced in this work. By integrating this warping concept to the recently developed directionally smoothed Wigner distribution algorithm [I], the high performance of that algorithm on linear, chirp-like components is extended to signal components with curved time-frequency supports. The main advantage of the algorithm is its ability to suppress not only the cross-cross terms, but also the auto-cross terms in the Wigner distribution. For a signal with N samples duration, the computational complexity of the algorithm is O(N log N) flops for each computed slice of the new time-frequency distribution.
Foveated imaging has been explored for compression and tele-presence, but gaps exist in the study of foveated imaging applied to acquisition and tracking systems. Results are presented from two sets of experiments com...
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ISBN:
(纸本)9780819475879
Foveated imaging has been explored for compression and tele-presence, but gaps exist in the study of foveated imaging applied to acquisition and tracking systems. Results are presented from two sets of experiments comparing simple foveated and uniform resolution targeting (acquisition and tracking) algorithms. The first experiments measure acquisition performance when locating Gabor wavelet targets in noise, with fovea placement driven by a mutual information measure. The foveated approach is shown to have lower detection delay than a notional uniform resolution approach when using video that consumes equivalent bandwidth. The second experiments compare the accuracy of target position estimates from foveated and uniform resolution tracking algorithms. A technique is developed to select foveation parameters that minimize error in Kalman filter state estimates. Foveated tracking is shown to consistently outperform uniform resolution tracking on an abstract multiple target task when using video that consumes equivalent bandwidth. Performance is also compared to uniform resolution processing without bandwidth limitations. In both experiments, superior performance is achieved at a given bandwidth by foveated processing because limited resources are allocated intelligently to maximize operational performance. These findings indicate the potential for operational performance improvements over uniform resolution systems in both acquisition and tracking tasks.
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