3,129 research outputs found

    IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution

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    Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image- based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT), IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases (LDB) algorithm in combination with Fisher’s criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%

    Underwater Fish Detection with Weak Multi-Domain Supervision

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    Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower accuracy when applied to new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN needs to be continuously fine-tuned to improve its classification accuracy to handle new project-specific fish species or habitats. In this work we present a labelling-efficient method of training a CNN-based fish-detector (the Xception CNN was used as the base) on relatively small numbers (4,000) of project-domain underwater fish/no-fish images from 20 different habitats. Additionally, 17,000 of known negative (that is, missing fish) general-domain (VOC2012) above-water images were used. Two publicly available fish-domain datasets supplied additional 27,000 of above-water and underwater positive/fish images. By using this multi-domain collection of images, the trained Xception-based binary (fish/not-fish) classifier achieved 0.17% false-positives and 0.61% false-negatives on the project's 20,000 negative and 16,000 positive holdout test images, respectively. The area under the ROC curve (AUC) was 99.94%.Comment: Published in the 2019 International Joint Conference on Neural Networks (IJCNN-2019), Budapest, Hungary, July 14-19, 2019, https://www.ijcnn.org/ , https://ieeexplore.ieee.org/document/885190

    Crystal image analysis using 2D2D synchrosqueezed transforms

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    We propose efficient algorithms based on a band-limited version of 2D synchrosqueezed transforms to extract mesoscopic and microscopic information from atomic crystal images. The methods analyze atomic crystal images as an assemblage of non-overlapping segments of 2D general intrinsic mode type functions, which are superpositions of non-linear wave-like components. In particular, crystal defects are interpreted as the irregularity of local energy; crystal rotations are described as the angle deviation of local wave vectors from their references; the gradient of a crystal elastic deformation can be obtained by a linear system generated by local wave vectors. Several numerical examples of synthetic and real crystal images are provided to illustrate the efficiency, robustness, and reliability of our methods.Comment: 27 pages, 17 figure

    Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance

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    Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016

    Multispectral persistent surveillance

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    The goal of a successful surveillance system to achieve persistence is to track everything that moves, all of the time, over the entire area of interest. The thrust of this thesis is to identify and improve upon the motion detection and object association aspect of this challenge by adding spectral information to the equation. Traditional motion detection and tracking systems rely primarily on single-band grayscale video, while more current research has focused on sensor fusion, specifically combining visible and IR data sources. A further challenge in covering an entire area of responsibility (AOR) is a limited sensor field of view, which can be overcome by either adding more sensors or multi-tasking a single sensor over multiple areas at a reduced frame rate. As an essential tool for sensor design and mission development, a trade study was conducted to measure the potential advantages of adding spectral bands of information in a single sensor with the intention of reducing sensor frame rates. Thus, traditional motion detection and object association algorithms were modified to evaluate system performance using five spectral bands (visible through thermal IR), while adjusting frame rate as a second variable. The goal of this research was to produce an evaluation of system performance as a function of the number of bands and frame rate. As such, performance surfaces were generated to assess relative performance as a function of the number of bands and frame rate

    A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

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    Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

    Digital Rock Reconstruction And Property Calculation Of Fractured Shale Rock Samples

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    As the preferential flow channels in the shale reservoir, the fracture systems including the natural micro-cracks and hydraulic fractures have received great attention from the whole energy industry worldwide. However, it is challenging to quantify the fracture systems in the shale rocks precisely because most of well-developed “histogram-based” image processing techniques cannot handle the case of small target segmentation. Because the fracture apertures are very thin, the over-segmentation or insufficient segmentation would lead to significant error in the quantification, including the fracture porosity, aperture, length, tortuosity etc., which would lead to serious mistakes to the property calculation. In this research, two novel image processing methods are proposed. The self-adaptive image enhancement method employs incomplete beta function and simulated annealing algorithm to modify the grayscale intensity histogram. The contrast between the target and the background of the transformed gray image reaches the maximum. Also, “self-adaptive” means the enhancement process is specified by the input images. The comparison of segmentation results before and after the image enhancement show that the target becomes more obvious to the naked eyes and the precise fracture porosity of the test image is 4.02 %. The multi-stage image segmentation (MSS) method combines the global and local information of the image to finish the segmentation. The generated three-dimensional model provides visualization of the fracture systems existing in the core. Also, the important parameters of the fractures can be obtained, including aperture, length, tortuosity, and porosity. Compared with the real permeability from the core-flooding experiments, the permeability calculated from the MSS method has the minimum error of 22.1 %. The results show that the proposed methods in this research can be effective tools for the precise quantification of the thin fracture systems
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