A Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is an effective way to identify human users and prevent cyber-attacks. In this research, we present a novel approach for generating...
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A Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is an effective way to identify human users and prevent cyber-attacks. In this research, we present a novel approach for generating and validating handwritten Arabic CAPTCHAs. With high Internet penetration among Arabic speakers, Arabic CAPTCHAs have recently gained interests among the researchers. Handwritten text based CAPTCHAs have been shown as more secured compared to printed text based CAPTCHAs. Therefore, the proposed method uses synthesized handwritten Arabic words to generate CAPTCHAs. In the user-validation phase of our method, users solve the CAPTCHAs by finding the segmentation points in the cursive Arabic words. To the best of our knowledge, such a segmentation-validation based approach for CAPTCHAs is reported for the first time in this paper. In addition, this work introduces the use of synthesized words for handwritten Arabic CAPTCHAs. The proposed method is sufficiently flexible to generate CAPTCHAs with various degrees of difficulty. The usability and security aspects of the current method have been evaluated through experimental studies with very promising results. (C) 2020 Elsevier Ltd. All rights reserved.
The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for ...
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The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for the complex signal. In this case, wavelet decomposition is used to decompose the power load into a series of sub signals. The low frequency sub signal is remarkably periodic, and the high frequency sub signals can prove to be chaotic signals. Then the signals of different characteristics are predicted by different models. For the low frequency sub signal, the support vector machine (SVM) is adopted. In SVM model, air temperature and week attributes are included in model inputs. Especially the week attribute is represented by a 3-bit binary encoding, which represents Monday to Sunday. For the chaotic high frequency sub signals, the chaotic local prediction (CLP) model is adopted. In CLP model, the embedding dimension and time delay are key parameters, which determines the prediction accuracy. In order to find the optimal parameters, a segmentation validation algorithm is proposed in this paper. The algorithm segments the known power load according to the time sequence. Then, based on the segmentation data, the optimal parameters are chosen based on the prediction accuracy. Compared with a single model, the prediction accuracy of the proposed algorithm is improved obviously, which proves the effectiveness.
We developed a novel method for spatially-local selection of atlas-weights in multi-atlas segmentation that combines supervised learning on a training set and dynamic information in the form of local registration accu...
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We developed a novel method for spatially-local selection of atlas-weights in multi-atlas segmentation that combines supervised learning on a training set and dynamic information in the form of local registration accuracy estimates (SuperDyn). Supervised learning was applied using a jackknife learning approach and the methods were evaluated using leave-N-out cross-validation. We applied our segmentation method to hippocampal segmentation in 1.5T and 3T MRI from two datasets: 69 healthy middle-aged subjects (aged 4449) and 37 healthy and cognitively-impaired elderly subjects (aged 72-84). Mean Dice overlap scores (left hippocampus, right hippocampus) of (83.3, 83.2) and (85.1, 85.3) from the respective datasets were found to be significantly higher than those obtained via equally-weighted fusion. STAPLE, and dynamic fusion. In addition to global surface distance and volume metrics, we also investigated accuracy at a spatially-local scale using a surface-based segmentation performance assessment method (SurfSPA), which generates cohort-specific maps of segmentation accuracy quantified by inward or outward displacement relative to the manual segmentations. These measurements indicated greater agreement with manual segmentation and lower variability for the proposed segmentation method, as compared to equally-weighted fusion. (C) 2011 Elsevier Inc. All rights reserved.
Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presentin...
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Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors). (C) 2008 Elsev
作者：
Eldeib, AMCairo Univ
Fac Engn Syst & Biomed Engn Dept Giza 12613 Cairo Egypt
This paper presents a 3-D visualization tool that manipulates and/or enhances by user input the segmented targets and other organs. A 3-D visualization tool is developed to create a precise and realistic 3-D model fro...
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ISBN:
(纸本)0819444294
This paper presents a 3-D visualization tool that manipulates and/or enhances by user input the segmented targets and other organs. A 3-D visualization tool is developed to create a precise and realistic 3-D model from CT/MR data set for manipulation in 3-D and permitting physician or planner to look through, around, and inside the various structures. The 3-D visualization tool is designed to assist and to evaluate the segmentation process. It can control the transparency of each 3-D object. It displays in one view a 2-D slice (axial, coronal, and/or sagittal) within a 3-D model of the segmented tumor or structures. This helps the radiotherapist or the operator to evaluate the adequacy of the generated target compared to the original 2-D slices. The graphical interface enables the operator to easily select a specific 2-D slice of the 3-D volume data set. The operator is enabled to manually override and adjust the automated segmentation results. After correction, the operator can see the 3-D model again and go back and forth till satisfactory segmentation is obtained. The novelty of this research work is in using state-of-the-art of image processing and 3-D visualization techniques to facilitate a process of a medical volume segmentation validation and assure the accuracy of the volume measurement of the structure of interest.
Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease, In this paper, a new paradigm for guided edge delineation is described, which involves presenting...
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Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease, In this paper, a new paradigm for guided edge delineation is described, which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, we found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers, The use of edge guidance for boundary delineation call also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques.
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