High-definition Images Research Articles

Digital imaging of the oil permeation mechanism in an oleophobic textile

Resistance of military clothing to oil permeation is important for effective protection against chemical warfare. In this paper, while a military textile is rendered oleophobic (oil contact angle ≈ 120°) through plasma-assisted deposition of perfluorodecyl acrylate (PFAC8), permeation of the textile by silicon oil is observed. Using high-definition digital imaging, we study the oil permeation dynamics, rationalize the permeation with a plausible mechanism and identify the threshold textile pore size for prevention of the permeation. We find that oil permeates defects of PFAC8 textiles. Our data suggests a linear variation for oil permeation volume ΔV with time t and implies a gravity-driving permeation mechanism. The mechanism comprises three stages involving merging and propagation of oil–yarn contact lines. The threshold pore size dm scales with σ/ P, where σ is the oil surface tension and P is the hydrostatic head exerted by the oil drop. The paper indicates the importance of an undamaged textile structure to ‘robust’ oil resistance.

Usefulness of high definition sialoendoscope for evaluation of lacrimal drainage system in congenital nasolacrimal duct obstruction.

Sialoendoscope was used as a dacryoendoscope, high- definition images of the lacrimal drainage system (LDS) were captured and its performance in congenital nasolacrimal duct obstruction (CNLDO) is reported. Nasal endoscopy was done as the first in all the cases using a 0°, 2.7 mm nasal endoscope (Karl Stroz Tutlingan Germany). This was followed by Dacryoendoscopy (DEN) of the lacrimal drainage system from puncta till the valve of Hasner. DEN was performed under general anaesthesia in 26 children (including 17 primary and 9 failed probing cases). All cases were examined using 0.8 mm sialoendoscope (Karl Storz, Tuttlingen, Germany) with fibreoptic light transmission with a side port for irrigation. We were able to obtain high-definition images of canaliculi, lacrimal sac, sac duct junction, proximal, mid and distal segments of nasolacrimal duct in all the children with CNLDO.In 17 primary cases 16 had distal membranous obstruction and I case had proximal canalicular obstruction. In nine children with history of failed probing, five had membranous obstruction, two had dacryoliths, two had NLD malformation at different levels. An overall success rate of 88.4% was achieved. Sialoendoscope can be used as high-definition dacryoendoscope for diagnostic and therapeutic use in CNLDO. Useful information can be obtained on dacryoendoscopy in complex CNLDO cases.

Monitoring rocky reef biodiversity by underwater geo-referenced photoquadrats

Digital images are an excellent tool for divers to sample hard-bottom subtidal habitats as bottom time is limited and high-definition images can be collected quickly and accurately. The present paper describes a sampling protocol for benthic rocky reef communities using geo-referenced photoquadrats and tests the method over several rocky reefs of Atlantic Patagonia. This method was tested in two localities, separated by 100 km in a semi-enclosed gulf, covering a total of 5800 m of 11 rocky reefs using track roaming transects. The protocol is non-destructive, relatively low-cost and can adequately assess changes in marine habitats as rocky reefs. The implementation of artificial intelligence analysis using human expert training may reduce analysis time and increase the amount of data collected. The present study recommends this sampling methodology for programs aimed at monitoring changes in biodiversity.

Digital Cholangioscopic Interpretation: When North Meets the South

Digital single-operator cholangioscopy (DSOC) (SpyGlass DS™, Boston Scientific, MA, USA) allows for high-definition imaging of the biliary tree. The superior visualization has led to the development of two different sets of criteria to evaluate and classify indeterminate biliary strictures: the Monaco criteria and the criteria in Carlos Robles-Medranda's publication (CRM). Our objective was to assess the interrater agreement (IA) of DSOC interpretation for indeterminate biliary strictures using the two newly published criteria. Forty de-identified DSOC video recordings were sent to 15 interventional endoscopists with experience in cholangioscopy. They were asked to score the videos based on the presence of Monaco Classification criteria: stricture, lesion, mucosal changes, papillary projections, ulceration, white linear bands or rings, and vessels. Next, they scored the videos using CRM criteria: villous pattern, polypoid pattern, inflammatory pattern, flat pattern, ulcerate pattern and honeycomb pattern. The endoscopists then diagnosed the recordings as neoplastic or non-neoplastic based on the criteria. Intraclass correlation (ICC) analysis was done to evaluate interrater agreement for both criteria set and final diagnosis. Recordings of 26 malignant lesions and 14 benign lesions were scored. The IA using both the Monaco criteria and CRM criteria ranged from poor to excellent (range 0.1-0.76) and (range 0.1-0.62), respectively. Within the Monaco criteria, IA was excellent for lesion (0.75) and fingerlike papillary projections (0.74); good for tortuous vessels (0.7), mucosal features (0.62), uniform papillary projections (0.53), and ulceration (0.58); and fair for white linear bands (0.4). Within the CRM criteria, the IA was good for villous pattern (0.62), flat pattern (0.62), and honeycomb pattern; fair for ulcerated pattern (0.56), polypoid pattern (0.52) and inflammatory pattern (0.54). The diagnostic IA using Monaco criteria was good (0.65), while the diagnostic IA using CRM was fair (0.58). The overall diagnostic accuracy using the Monaco classification was 61% and CRM criteria were 57%. The IOA and accuracy rate of DSOC using visual criteria from both Monaco Criteria and CRM are similar. However, some criteria from both sets suffer from poor IA, thus affecting the overall diagnostic accuracy. More formal training and refinements in visual criteria with additional validation are needed to improve diagnostic accuracy. ClinicalTrials.gov Identifier: NCT02166099.

IRISH FILM AND TELEVISION THE YEAR IN REVIEW – 2020

Part of the appeal of cinema-going since its inception, especially with the emergence of purpose built picture palaces designed to appeal to more well-heeled patrons, has been the opportunity to seal oneself into a dark, womb-like environment and succumb the stimulation of the optical and auditory senses by high definition images and crystal clear sounds. In

Perceptual Quality Evaluation of Hazy Natural Images

Haze is an intrusion element that disrupts color fidelity and contrast of outdoor natural images, affecting their perceptual quality. The differential characteristics of hazy images compared to other natural images restrict the generalization of existing image quality assessment (IQA) algorithms. At the same time, efficient IQA algorithms for predicting the perceptual quality of naturally hazed images have not been proposed in the literature due to lack of a relevant dataset. To address this, we build the IIT-JMU Hazy Image Dataset comprising of 1000 high-definition hazy natural images consisting of diverse categories such as landscape, forests, roads, seascapes, and cityscapes, along with their subjective quality ratings. We present an analysis of existing natural-scene-statistics-based IQA algorithms on hazy natural images. In this article, we propose a convolutional-neural-network-based quality assessment algorithm for hazy natural images along with an IQA metric called deep learning-based haze perceptual quality evaluator (DLHPQE). The proposed DLHPQE efficiently predicts the perceptual quality of hazy natural images without a reference. Our results demonstrate that the DLHPQE outperforms existing state-of-the-art no-reference IQAs in terms of several performance parameters such as Pearson linear correlation coefficient, Spearman rank-order correlation coefficient, Kendall's rank-order correlation coefficient, and root-mean-square error.

A comparative study between high-definition volumetric imaging computed tomography and multi-slice computed tomography in the detection of acute thoraco-lumbar disc extrusions in dogs

Computed tomography (CT) is commonly used to image intervertebral disc extrusion (IVDE) in dogs. The current gold standard for CT imaging is the use of multi-slice CT (MS CT) units. Smaller high-definition volumetric imaging (HDVI) mobile CT has been marketed for veterinary practice. This unit is described as an advanced flat panel. The goal of this manuscript was to evaluate the ability of the HDVI CT in detecting IVDE without the need for CT myelography, compared with the detection of acute disc extrusions with a MS CT without the need for MS CT myelogram. Retrospective blinded analyses of 219 dogs presented for thoraco-lumbar IVDE that had a HDVI CT (n = 123) or MS CT (n = 96) were performed at a single referral hospital. A total of 123 cases had HDVI CT scans with surgically confirmed IVDE. The IVDE was identified in 88/123 (72%) dogs on pre-contrast HDVI CT. The remaining 35/128 (28%) cases required a HDVI CT myelogram to identify the IVDE. Ninety-six cases had MS CT scans with surgically confirmed IVDE. The IVDE was identified in 78/96 (81%) dogs on the pre-contrast MS CT. The remaining 18/96 (19%) cases had a MS CT myelogram to identify the IVDE. Multi-slice CT detected IVDE significantly more than HDVI CT (p = 0.032). This study showed that the ability of HDVI CT for detecting IVDE is lower than that of MS CT. The HDVI CT system may be useful in smaller referral practices, with a lower case load where space is limited.

Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations

Digital analysis of cancer specimens using spectroscopic imaging coupled to machine learning is an emerging area that links spatially localized spectral signatures to tissue structure and disease. In this study, we examine the role of spatial-spectral tradeoffs in infrared spectroscopic imaging configurations for probing tumors and the associated microenvironment profiles at different levels of model complexity. We image breast tissue using standard and high-definition Fourier Transform Infrared (FT-IR) imaging and systematically examine the localization, spectral origins, and utility of data for classification. Results demonstrate that higher spatial detail provides high sensitivity and specificity of tissue segmentation, despite the increased subcellular variability. High definition imaging also allows accurate analysis of complex, multiclass models of breast tissue without compromising accuracy. A comparison of results also highlights the key differences in the data distributions and classification performance across modalities to better guide decision making for acquiring and analyzing IR imaging data for specific histopathological models.

Otosurgery with the High-Definition Three-Dimensional (3D) Exoscope: Advantages and Disadvantages.

Background: The aim of the study was to describe our initial experience with the high-definition three-dimensional (3D) exoscope for middle ear surgery versus the operating microscope. Methods: The study included 60 randomly chosen patients diagnosed with otosclerosis (n = 30) or chronic otitis media (n = 30) with a clinical indication for surgery. The primary measurement was the subjective estimation of quality of the visibility of the operating field provided by the 3D exoscope—VITOM-3D (Karl Storz, Tuttlingen, Germany) in comparison to the operating microscope. Results: All procedures, except for two (3.3%) converted to the microscope, were successfully completed using a 3D exoscope. In both stapedotomy and tympanoplasty, the exoscope was superior to the microscope during more superficial portions of the procedures. By contrast, in deeper areas of the middle ear, the exoscope provided significantly worse visibility, but usually not suboptimal. Both intraoperative bleeding and the narrow surgical field substantially reduced the visibility with the 3D exoscope in comparison to the microscope. Conclusions: Overall, our study shows that the 3D exoscope offers excellent, highly magnified, and well-illuminated high-definition images of the surgical field. However, our experience revealed several important limitations of this system, including decreased depth perception in deep areas of the tympanic cavity and reduced visibility in a difficult surgical field, with subsequent need to switch to an operating microscope in select cases.

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Subjective and no-reference quality metric of domain independent images and videos

There has been an unprecedented explosion in creation and consumption of high definition videos and images. This has led to the development and adoption of several systems which manipulate graphic content. Most of these systems require a robust, no-reference, domain independent, and subjective quality metric. The state-of-the-art algorithms aimed at no-reference quality assessment of images and videos employ neural networks that are content domain dependent and require application specific training. Further, present systems are trained to estimate quality metric in accordance to objective metrics such as Peak Signal to Noise Ratio or Mean Squared Error. We propose a convolutional neural network architecture that leverages a variable length long short term memory. The model will also make use of the Laplacian as a fourth layer in the input to ensure content domain independence in quality estimation. The system has been trained on the images of KonIQ-10K dataset and tested on KonViD-1k video dataset and LIVE video dataset. It has also been tested on 1000 frames and videos from the VIRAT dataset and images from BSD300 and Set5 datasets with artificial blur and distortion applied. The quality estimations were compared to the mean opinion square given by five human viewers and resulted in a 11% root mean squared error. Thus the proposed model provides a subjective quality estimation of graphic content without any dependence on the domain of the content.

Integral 3D/2D partially convertible display using geometric phase lens array

Conversion between three-dimensional (3D) and high-definition two-dimensional (2D) images is required to reproduce various video content. Conventional 3D/2D conversion methods based on integral 3D displays mainly aim to convert between 3D and 2D images on the entire display screen. Here, we propose a display method for partial conversion between 3D and 2D images using a geometric phase lens (GPL) array for the backlight control of a liquid crystal (LC) panel. In our method, the backlight is switched between a point light source (PLS) array and diffused light using a GPL array, diffusion plate, and polarization switching element. Integral 3D and high-definition 2D images are simultaneously reproduced on the same screen by displaying elemental images and 2D images on the LC panel according to the state of the backlight. Through numerical simulation experiments, we analyzed the resolution characteristics of 3D images and obtained the design policy of the PLS size and pixel size of the LC panel. Based on the analysis results, we prototyped a display system using a monochromatic LC panel with 8K resolution and evaluated the quality of backlight switching and resolution characteristics of 3D images. Further, we confirmed that 3D images with approximately 16,000 pixels and 2D images with 8K resolution are partially convertible.

Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Three-dimensional thermal mapping from aerial images can be used in energy audits. Tie points that define the location of object points in a 3D space for reconstructing a 3D model, also include thermal information, which plays an important role in energy audits. However, it is often harder and less accurate to extract common features and determine tie points from low-resolution thermal images. It is more effective and accurate to use high-definition RGB images to determine tie points and fuse the RGB and thermal information. In this study, we investigate how to utilize high-definition RGB images that allow for more accurate tie point detection, how different flight configurations affect tie point data fusion, and how tie point data fusion performance can be improved. We propose a tie points' thermal and RGB data-fusion framework to create district-level thermal mapping to solve such problems. This paper aims to evaluate how different flight configurations affect the results of the proposed data fusion approach. Flight configurations include different camera altitudes (60 m and 35 m), distinct camera angles (45 degrees and 30 degrees), diverse flight path designs (mesh grid and Y path), and various building styles (campus buildings and city buildings).We find the following results in this paper: (1) higher flight altitude is not suggested for our data fusion approach; (2) a 30-degree thermal camera angle is suggested for roof inspection, while a 45-degree thermal camera angle is suggested for façade inspection when using the tie point data fusion approach; (3) a Y flight path performs better than a mesh grid path; and (4) our tie point data fusion approach performs better in traditional European city buildings than in modern campus buildings. We also demonstrate that pixels in the thermal images' central area can more accurately represent thermal information than pixels around the image edges for tie point data fusion. Additionally, our studies show that images taken at the edges of mapping areas have more errors. Thus, it is crucial to enlarge the survey area to obtain more accurate possible results.

The VITOM® exoscope in oculoplastic surgery: the 5 year Coventry experience.

BackgroundIntraoperative imaging is a desirable tool in oculoplastic surgery; however, there are few modalities which allow surgeons to capture and edit the images in real time without compromising the sterility of the surgical field. We describe the set-up of the VITOM® exoscope system based on our 5 years’ experience of using it as an effective intraoperative video imaging and teaching modality for extraocular surgery.MethodsThe VITOM® is a specially designed exoscope mounted onto a versatile mechanical arm. It is attached to a high definition (HD) digital camera displayed on the HD video monitor of a standard endoscopy stack. This technology has been utilised in other surgical subspecialties, but there is no documented use within extraocular surgery.ResultsThe exoscope was simple to set-up and allowed real-time recording and editing with an HD image display system. The theatre team were able to view the precise surgical steps, contributing to improved theatre flow. Trainees reported that the VITOM® images significantly improved their visualisation and understanding of key surgical anatomy and steps.ConclusionsOur experience showed that the VITOM® exoscope is an excellent intraoperative video imaging and teaching aid, as it allows real-time capture and editing of open surgery and seems to improve theatre flow. With newer models using 3D stereoscopic vision, it could be further evaluated as a heads-up viewing system within extraocular and oculoplastic surgery.

A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks

Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams.

High definition images transmission through single multimode fiber using deep learning and simulation speckles

Multimode fiber (MMF) plays a vital role in promoting the miniaturization of endoscope. However, real-time and high-definition imaging using the MMF that remains a challenging research. Traditional phase compensation and transmission matrix methods are affected by fiber shape and optical devices, which results in low imaging rate and accuracy. Deep learning can be used to construct the inverse transformation matrix (ITM, output to input) of the MMF. However, deep learning requires high similarity between sample sets. In this paper, we combine principal component analysis (PCA) method, deep learning based speckle classification (DLSC) and deep learning based image enhancement (DLIE) to improve imaging definition. To save experimental costs, we use the inverse-PCA method to obtain simulation speckles. The experimental results show that simulation speckles can be used for classification and image reconstruction of experimental speckles. With the difference between simulation and experimental speckles, which brings about low imaging definition. Therefore, we use the DLIE methods to further improve imaging definition. The experimental results show imaging capability with high definition for complex natural scenes, which may provide a feasible method for high definition images transmission through the MMF.

Fusion and Correction of Multi-Source Land Cover Products Based on Spatial Detection and Uncertainty Reasoning Methods in Central Asia

Land cover products are an indispensable data source in land surface process research, and their accuracy directly affects the reliability of related research. Due to the differences in factors such as satellite sensors, the temporal–spatial resolution of remote sensing images, and landcover interpretation technologies, various recently released land cover products are inconsistent, and their accuracy is usually insufficient to meet application requirements. This study, therefore, established a fusion and correction method for multi-source landcover products by combining them with landcover statistics from the Food and Agriculture Organization of the United Nations (FAO), introducing a spatial consistency discrimination technique, and applying an improved Dempster-Shafer evidence fusion method. The five countries in Central Asia were used for a method application and verification assessment. The nine products selected (CCI-LC, CGLS, FROM-GLC, GLCNMO, MCD12Q, GFSAD30, PALSAR, GSWD, and GHS-BUILT) were consistent in time and covered the study area. Based on the interpretation of 1437 high-definition image verification areas, the overall accuracy of the fusion landcover result was 85.32%, and the kappa coefficient was 0.80, which was better than that of the existing comprehensive products. The spatial consistency fusion method had the advantage of an improved statistical fitting, with an overall similarity statistic of 0.999. The improved Dempster-Shafer evidence theory fusion method had an accuracy that was 4.86% higher than the spatial consistency method, and the kappa coefficient increased by 0.07. Combining these two methods improved the consistency of the multi-source data fusion and correction method established in this paper and will also provide more reliable basic data for future research in Central Asia.

Fluoroless intravascular ultrasound image-guided liver navigation in porcine models

BackgroundAn intravascular ultrasound catheter (IVUSc) was developed for intracardiac ultrasound to assess interventions with compelling results. However, intrahepatic vascular exploration was rarely tested and was always associated with X-ray techniques. The aim of this study was to demonstrate the feasibility to navigate through the whole liver using an IVUSc, providing high-quality images and making it unnecessary to use ionizing radiation.MethodsAn ex vivo pig visceral block and an in vivo pig model were used in this study. The IVUS equipment was composed of an US system, and of an 8 French lateral firing IVUSc capable of producing 90-degree sector images in the longitudinal plane. After accessing the intravascular space with the IVUSc into the models, predetermined anatomical landmarks were visualized from the inferior vena cava and hepatic veins and corroborated.ResultsIVUS navigation was achieved in both models successfully. The entire navigation protocol took 87 and 48 min respectively, and 100% (21/21) and 96.15% (25/26) of the landmarks were correctly identified with the IVUSc alone in the ex vivo and in vivo models respectively. IVUS allowed to clearly visualize the vasculature beyond third-order branches of the hepatic and portal veins.ConclusionsA complete IVUS liver navigation is feasible using the IVUSc alone, making it unnecessary to use ionizing radiation. This approach provides high-definition and real-time images of the complex liver structure and offers a great potential for future clinical applications during diagnostic and therapeutic interventions.

3D Printing of Diffuse Low-Grade Gliomas Involving Eloquent Cortical Areas and Subcortical Functional Pathways: Technical Note

Surgical resection of diffuse low-grade gliomas (DLGGs) involving cortical eloquent areas and subcortical functional pathways represents a challenge in neurosurgery. Patient-specific, 3-dimensional (3D)-printed models of head and brain structures have emerged in recent years as an educational and clinical tool for patients, doctors, and surgical residents. Using multimodal high-definition magnetic resonance imaging data, which incorporates information from specific task-based functional neuroimaging and diffusion tensor imaging tractography and rapid prototyping technologies with specialized software and "in-house" 3D printing, we were able to generate 3D-printed head models that were used for preoperative patient education and consultation, surgical planning, and resident training in 2 complicated DLGG surgeries. This 3D-printed model is rapid prototyped and shows a means to model individualized, diffuse, low-level glioma in 3D space with respect to cortical eloquent areas and subcortical pathways. Survey results from 8 surgeons with different levels of expertise strongly support the use of this model for surgical planning, intraoperative surgical guidance, doctor-patient communication, and surgical training (>95% acceptance). Spatial proximity of DLGG to cortical eloquent areas and subcortical tracts can be readily assessed in patient-specific 3D printed models with high fidelity. 3D-printed multimodal models could be helpful in preoperative patient consultation, surgical planning, and resident training.

A Full-Featured FPGA-Based Pipelined Architecture for SIFT Extraction

Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps.