Video multiple watermarking technique based on image interlacing using DWT.
Ontology highlight
ABSTRACT:
SUBMITTER: Ibrahim MM
PROVIDER: S-EPMC4283264 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:INTRODUCTION:Over the past decades many innovations were introduced in total knee arthroplasty (TKA) focusing on implant longevity and higher procedural precision; however, there are still a high number of dissatisfied patients. It was reported that better anatomical alignment may result in improved patient outcome; however, current technologies have limitations to achieve this. The aim of this video article is to describe the technique of individualized alignment in TKA with the use of image-based robotic assistance. METHODS:The technology is based on an individual patient knee model computed from segmented computed tomography (CT) scans. A preoperative planning of prosthesis position is conducted following the principle of kinematic alignment. Intraoperatively the soft tissue envelope is recorded and the computer predicts the gap balance based on the virtual planning. The prosthesis position is then adapted to achieve balanced gaps and to avoid soft tissue release. This technique is shown in a cadaver operation and clinical examples of two patients are described. RESULTS:With the combination of anatomically oriented prosthesis positioning and minor adaptations with respect to the soft tissue, an individualized alignment is achieved with reduced need of soft tissue release. The robotic-assisted surgery guarantees a precise implementation of the planning. The initial experience showed a promising outcome in short-term follow-up.
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Project description:Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.
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Project description:Perceptual systems can be altered by immersing observers in environments with statistical properties that differ from those naturally encountered. Here we present a novel method for placing observers in naturalistic audio visual environments whose statistics can be manipulated in very targeted ways. We present the results of a case study that used this method. Observers were exposed to an environment where there was a novel statistical relationship between two simple, visual patterns in otherwise natural scenes. Exposure to this altered environment strengthened perceptual interactions between the two patterns.
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Project description:IntroductionThe introduction of new technologies and teaching strategies to educate the digital learner creates the potential for a better and more standardized training experience across programs. Thus, we sought to create an instructional video on stereotactic core breast biopsy that simulates best practices and could be readily accessed by training programs to improve and standardize resident education.MethodsAt our institution, we use the video and questions as part of a flipped classroom educational activity. Residents are requested to complete the questions at home and watch the video. They then take the posttest questions during the lecture period, at which time the answers are reviewed and there is additional discussion of the procedure. The tests contain both video-related questions and control questions.ResultsAfter viewing the video, there was a 30% mean improvement in the video-related questions compared to a 0% improvement in the control set of questions.DiscussionAn instructional video on stereotactic core biopsy was created with associated assessment questions that can be used at any institution for the purposes of improving and standardizing training in breast imaging procedures. This resource can be especially useful for programs where trainees have limited procedure exposure.
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Project description:Automatically generating a summary for asynchronous data can help users to keep up with the rapid growth of multi-modal information on the Internet. However, the current multi-modal systems usually generate summaries composed of text and images. In this paper, we propose a novel research problem of text-image-video summary generation (TIVS). We first develop a multi-modal dataset containing text documents, images and videos. We then propose a novel joint integer linear programming multi-modal summarization (JILP-MMS) framework. We report the performance of our model on the developed dataset.
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Project description:IntroductionPonseti casting method is the gold standard for the management of congenital talipes equinovarus in neonates, which consists of weekly manipulation and serial casting. Using oscillatory saw for cast removal is user-dependant and has been fraught with complications consisting of saw burns, thermal abrasions, anxiety, and fear of the saw (especially in children). The aim of this article is to describe our technique of cast removal using visual media format.TechniqueThis video describes an easy method of cast soakage with lukewarm water. Once the cast is soaked in water, the knob is identified, and the loosened outer layer of the cast is unwrapped in layers. Complete procedure can be done at home by parents. Since no equipment is utilized, the technique eliminates all complications associated with an oscillating saw.ConclusionCast soakage with lukewarm water followed by unwrapping is a simple and cost-effective method of Ponseti cast removal which can be done by parents at home improving the overall satisfaction of the family.
| S-EPMC8051574 | biostudies-literature
Project description:ObjectiveTo assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction.Materials and methodsWe retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction).ResultsObjective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons.ConclusionApplication of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.
| S-EPMC7458859 | biostudies-literature
Project description:With the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data, which could be compared to known profiles. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks (e.g., acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns. We validated the fidelity and robustness of the MIEL platform across multiple cells lines and using dose-response curves, to insure the robustness of this approach for high content high throughput drug discovery. Focusing on alternative, non-cytotoxic, glioblastoma treatments, we demonstrated that the MIEL assay can identify epigenetically active drugs and classify them by molecular function. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce a set of desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.
2019-12-29 | GSE134045 | GEO
Project description:BackgroundDNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained.ResultsWe developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists.ConclusionsThis work presents an automated genotyping tool from DNA gel electrophoresis images, called GELect, which was written in Java and made available through the imageJ framework. With a novel automated image processing workflow, the tool can accurately segment lanes from a gel matrix, intelligently extract distorted and even doublet bands that are difficult to identify by existing image processing tools. Consequently, genotyping from DNA gel electrophoresis can be performed automatically allowing users to efficiently conduct large scale DNA fingerprinting via DNA gel electrophoresis. The software is freely available from http://www.biotec.or.th/gi/tools/gelect.
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Project description:Acromioclavicular (AC) joint dislocation is a common injury, particularly among active young individuals. Numerous surgical procedures for treating acute, high-grade AC joint dislocation have been reported. However, no standard surgical procedure that restores the normal kinematics of the AC joint is available. Among the available coracoclavicular (CC) fixation techniques, cortical button fixation was recently introduced, and it has had successful outcomes. Moreover, it is advantageous because it can be used in arthroscopic procedures. However, because of the limited number of fixation tools, a fundamental problem in terms of horizontal instability and gradual subsidence of cortical buttons has been observed, eventually leading to a threat to vertical stability. Therefore, we developed a unique CC fixation technique with multiple small tunnels using all-suture anchors, which may overcome potential complications caused by cortical buttons that require bone tunnels with relatively large diameters. This arthroscopic CC fixation technique was designed to achieve the ideal horizontal and vertical stability that may restore native AC kinematics.
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