8-bit Depth Research Articles

Implementation of Super Resolution Techniques in Geospatial Satellite Imagery

The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation. A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done.

Computed vs. film-based radiographs' contour artifacts influence diagnosis of secondary caries

To test local grey-scale changes on dental bitewing radiographs near filling margins for image acquisition. Forty approximal preparations in caries-free amalgam filled teeth and bitewing radiographs were acquired under standardized conditions applying four techniques. Film-based analog radiographs were digitized using flat-bed scanner (FDR). Phosphor-plate computed radiographs (PCR) were directly acquired by scanning VistaScan imaging plates. Image quality was tested using Preset Filter (PF) or manually applied IntraOral Fine Filter (IF) to enhance digital images. Local changes from digital imaging processing were assessed by comparing the margin-near (MN) and margin-far (MF) zone by a multivariate repeated measurements analysis. All images were acquired with 8-bit depth (256 shades). Dentine was displayed in 79 shades for FDR and 54 shades for PCR. PF or IF locally modify bitewing radiographs by darkening marginal dentine by 8 or 29 shades, respectively. The sharpest display of the margin (shades per pixel) from dentine to filling was found for IF (26.2), followed by FDR (23.2), PF (15.3) and PCR (8.3). Computed radiography with phosphor plates generate more homogeneous images compared to flatbed-digitized film-based radiographs. The filling margin was sharpest represented with the IF filter at the detriment of an artificial darkening of the dentine near the margin of the filling. Contour artifacts by filters have the potential to confound diagnosis of secondary caries. Algorithms and filters for sensor data processing, causing local changes above 2% of the dynamic range by non-continuous mathematical functions, should only be applied with caution, manually when diagnosing and reversibly.

FitDepth: fast and lite 16-bit depth image compression algorithm

This article presents a fast parallel lossless technique and a lossy image compression technique for 16-bit single-channel images. Nowadays, such techniques are “a must” in robotics and other areas where several depth cameras are used. Since many of these algorithms need to be run in low-profile hardware, as embedded systems, they should be very fast and customizable. The proposal is based on the consideration of depth images as surfaces, so the idea is to split the image into a set of polynomial functions that each describes a part of the surface. The developed algorithm herein proposed can achieve a similar—or better—compression rate and especially higher speed rates than the existing techniques. It also has the potential of being fully parallelizable and to run on several cores. This feature, compared to other approaches, makes it useful for handling and streaming multiple cameras simultaneously. The algorithm is assessed in different situations and hardware. Its implementation is rather simple and is carried out with LIDAR captured images. Therefore, this work is accompanied by an open implementation in C++.

Swept-source endoscopic optical coherence tomography real-time imaging system based on GPU acceleration for axial megahertz high-speed scanning.

In order to solve the problem of image real-time processing and correction for high-speed endoscopic swept-source optical coherence tomography (SS-OCT), we highly optimize a computer-unified device architecture-based platform and use a field-programmable gate array to summarize the application experience. We use the Half-Sync/Half-Asyn mode to optimize memory in order to build a high-throughput data thread pool for CPU. We use asynchronous streaming architecture to multiplex multiple threads at high speed to accelerate data processing. At the same time, we design a rotary scanning position information encoding feedback module to suppress image drift, which can realize 25ns logic-timing sequence synchronization control through FPGA 40MHz clock. The maximum complete attainable axial-scan-processing rate (including memory transfer and display of B-scan frames) is 3.52 MHz for a 16-bit pixel depth and A-scans/s of 1024 pixels. To our knowledge, this is the fastest processing rate reported to date with a single-chip graphical processing unit for SS-OCT. Finally, the established high-speed SS-OCT is used to image mouse esophagus and human fingers, and the output images are stable. When the image size is 1024 × 1024 pixels, the real-time imaging rate is 200 frames per second. This paper develops a real-time image processing and reconstruction technology suitable for high-throughput SS-OCT systems, which can have high-density operation and efficient parallelism, while suppressing high-speed image drift. It lays the foundation for the non-destructive, in vivo, non-staining, fast and convenient early tumor diagnosis of high-speed endoscopic SS-OCT.

Correlation of fixture placement and patient symptoms in orthognathic surgery: A retrospective analysis of post-surgical evaluations and comparisons of multiple surgery protocols

<h3>Objective</h3> This project aimed to analyze the proximity of orthognathic surgical fixation to specific anatomical landmarks and to assess postoperative symptoms. <h3>Study Design</h3> The data consisted of an available pool of CT scans from the oral surgery department at the University of Texas Health at San Antonio from 2015 to 2020. We investigated the proximity of orthognathic fixation to the maxillary sinus, nerve, and tooth. The inclusion criteria were full volume CBCT or MDCT scans, age of 14-60 years, primary orthognathic surgery. The exclusion criteria were past surgery or augmentation, congenital syndromes, acquired abnormalities, pathological lesions, and inadequate image quality. Screening of an available patient pool resulted in 400 patient scans. The pilot data consisted of 50 patients. We gathered information about patients' symptoms from their charts. We viewed the scans on the Dell NVIDIA Quadro P1000 23" monitor with 1920 × 1200 desktop resolution, 8-bit depth, and standard dynamic range for the image analysis. We performed measurements from the fixation to the closest point of the nearest vital structure (tooth, nerve, or sinus) on the Invivo6 version 6.0 Anatomage software, using axial, coronal, and sagittal views with the ruler tool to perform these measurements. We divided the measurements into three groups based on their proximity to the vital structures: A=0 mm, B≤2mm, and C>2mm. Finally, we compared patients' complaints and complications relative to the proximity of the surgical fixation to the vital structures. <h3>Results</h3> The preliminary data suggested a significant difference in symptoms associated with fixation at less than 2 mm. <h3>Conclusion</h3> We expect to find an increase in postoperative symptoms with proximity to the above-mentioned landmarks, to be determined by the chi-squared test for statistical analysis. Support of the hypothesis could encourage an emphasis on creating preoperative analysis standards affecting the identification, evaluation, and marking of vital structures on the images during preoperative surgical planning. <b>Statement of Ethical Review</b> Ethical Review or exemption was not warranted for this study

Sampling strategies for learning-based 3D medical image compression

Recent achievements of sequence prediction models in numerous domains, including compression, provide great potential for novel learning-based codecs. In such models, the input sequence’s shape and size play a crucial role in learning the mapping function of the data distribution to the target output. This work examines numerous input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16-bit depth) losslessly. The main objective is to determine the optimal practice for enabling the proposed Long Short-Term Memory (LSTM) model to achieve high compression ratio and fast encoding–decoding performance.Our LSTM models are trained with 4-fold cross-validation on 12 high-resolution CT dataset while measuring model’s compression ratios and execution time. Several configurations of sequences have been evaluated, and our results demonstrate that pyramid-shaped sampling represents the best trade-off between performance and compression ratio (up to 3×). We solve a problem of non-deterministic environments that allow our models to run in parallel without much compression performance drop.Experimental evaluation was carried out on datasets acquired by different hospitals, representing different body segments, and distinct scanning modalities (CT and MRI). Our new methodology allows straightforward parallelisation that speeds-up the decoder by up to 37× compared to previous methods. Overall, the trained models demonstrate efficiency and generalisability for compressing 3D medical images losslessly while still outperforming well-known lossless methods by approximately 17% and 12%. To the best of our knowledge, this is the first study that focuses on voxel-wise predictions of volumetric medical imaging for lossless compression.

A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging

Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic interventional procedures.

Staff dose management is a continuing concern in fluoroscopically-guided interventional (FGI) procedures. Being unaware of radiation scatter levels can lead to unnecessarily high stochastic and deterministic risks due to the effects of absorbed dose by staff members. Our group has developed a scattered-radiation display system (SDS) capable of monitoring system parameters in real-time using a controller-area network (CAN) bus interface and displaying a color-coded mapping of the Compton-scatter distribution. This system additionally uses a time-of-flight depth sensing camera to track staff member positional information for dose rate updates. The current work capitalizes on our body tracking methodology to facilitate individualized dose recording via human recognition using 16-bit grayscale depth maps acquired using a Microsoft Kinect V2. Background features are removed from the images using a depth threshold technique and connected component analysis, which results in a body silhouette binary mask. The masks are then fed into a convolutional neural network (CNN) for identification of unique body shape features. The CNN was trained using 144 binary masks for each of four individuals (total of 576 images). Initial results indicate high-fidelity prediction (97.3% testing accuracy) from the CNN irrespective of obstructing objects (face masks and lead aprons). Body tracking is still maintained when protective attire is introduced, albeit with a slight increase in positional data error. Dose reports are then able to be produced which contain cumulative dose to each staff member at the eye lens level, waist level, and collar level. Individualized cumulative dose reporting through the use of a CNN in addition to real-time feedback in the clinic will lead to improved radiation dose management.

An efficient medical image encryption using hybrid DNA computing and chaos in transform domain.

In this growing era, a massive amount of digital electronic health records (EHRs) are transferred through the open network. EHRs are at risk of a myriad of security threats, to overcome such threats, encryption is a reliable technique to secure data. This paper addresses an encryption algorithm based on integer wavelet transform (IWT) blended with deoxyribo nucleic acid (DNA) and chaos to secure the digital medical images. The proposed work comprises of two phases, i.e. a two-stage shuffling phase and diffusion phase. The first stage of shuffling starts with initial block confusion followed by row and column shuffling of pixels as the second stage. The pixels of the shuffled image are circularly shifted bitwise at the first stage of diffusion to enhance the security of the system against differential attack. The second stage of diffusion operation is based on DNA coding and DNA XOR operations. The experimental analyses have been carried out with 100 DICOM test images of 16-bit depth to evaluate the strength of the algorithm against statistical and differential attacks. By the results, the maximum entropy has been obtained an average of 15.79, NPCR of 99.99, UACI of 33.31, and larger keyspace of 10140, which infer that our technique overwhelms various other state-of-the-art techniques.

An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.

Pedestrian Detection in Severe Weather Conditions

Pedestrian detection has never been an easy task for computer vision and the automotive industry. Systems like the advanced driver-assistance system (ADAS) highly rely on far-infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what the performance in adverse weather conditions is. In this paper, we introduce a 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions. We also provide a synchronized Controller Area Network (CAN bus) data, including driving speed, brake pedal status, and outside temperature for future ADAS system development. Furthermore, we have tested and provided 16-bit depth modifications for the YOLOv3 deep neural network (DNN) based detector, reaching a mean Average Precision (mAP) up to 89.1%. The ZUT dataset is published and publicly available at IEEE Dataport and Github.

Low-complexity open-loop coding of IDR infrared images having JPEG compatibility

In this paper, we present a low-complexity open-loop Intermediate Dynamic Range (IDR) infrared image coding algorithm which provides two scalability layers. The first layer corresponds to a low dynamic range (LDR) version of the image which can be obtained by a JPEG decoder, while the second layer corresponds to the original IDR image. To achieve bit-depth scalability, we separate an input IDR image into LDR and residual images by simple operations, so that the residual image has a bit depth not higher than 8 bits per pixel, i.e., it can be compressed by JPEG baseline or other coding algorithm, developed for 8-bit depth formats. For real-time applications, we introduce a predefined look-up table containing quality factors for both the LDR and the residual images, so that increase of the table index corresponds to reduction of bit rate and increase of distortion close to an operational rate-distortion curve. Experimental results show that the proposed algorithm is significantly less complex than JPEG-XT Profile C, part 6, and provides better coding performance than its reference software with default tone mapping operator for the vast majority of the infrared test images.

Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively.

Reducing Pixel Range to Improve Image Compression Quality

Objective: This study aims to contribute in developing a compression method that performs both high quality and applicable compression ratio, which is a hard task and requires a lot of sequential experiments since each of the aimed characteristics belong to different group type of image compression techniques lossy and lossless. Therefore, the hybridization between those two types is the convenient solution because it allows the desired results to appear in one technique. Methods/Findings: The researcher proposed reducing the pixel range that represents the original image by a fixed rate, which yields to reducing the repetition and preserving a large amount of the data in this phase before proceeding to the lossy part, in this case, RIFD technique that will affect better reducing the compressed image size due to the previous step. The Calculations of the objective measurements MSE, MAE and PSNR used to assess the effect of the suggested method (RPR) by collecting the results which gained after applying it to different grayscale and colour images. Application: The proposed lossless part does not require complex calculations and the results proved its ability to improve the compression quality for the elected lossy technique without affecting the compression ratio. Comparisons showed significant improvement on grey scale images especially those having 16-bit depth and marginally in RGB images. Keywords: Compression Quality, Hybrid Compression, Objective Metrics, Reducing Pixel Range, Redundancy, RIFD Technique

A novel resolution independent gradient edge predictor for lossless compression of medical image sequences

ABSTRACT Digital visualization of human body in terms of medical images with high resolution and bit depth generates tremendous amount of data. In the field of medical diagnosis, lossless compression technique is preferred that facilitates efficient archiving and transmission of medical images avoiding false diagnosis. Among various approaches to lossless compression of medical images, predictive coding techniques have high coding efficiency and low complexity. Gradient Edge Detector (GED) used in predictive coding is based on threshold value for prediction and choice of threshold is very important for efficient prediction. However, no specific method is adopted in the literature for threshold value selection. This paper presents an efficient prediction solution targeted at lossless compression of 8 bits and higher bit depth volumetric medical images up to 16 bits. Novelty of the proposed technique is developing Resolution Independent Gradient Edge Predictor (RIGED) algorithm to support 8- and 16-bit depth medical images. Percentage improvement of the proposed model is 30.39% over state-of-the-art Median Edge Detector (MED) and 0.92% over Gradient Adaptive Predictor (GAP) in terms of entropy for medical image dataset of different modalities having different resolutions and bit depths.

Determination of computed tomography number of high-density materials in 12-bit, 12-bit extended and 16-bit depth for dosimetric calculation in treatment planning system

AbstractAimThe main aim was to examine the effect of bit depth on computed tomography (CT) number for high-density materials. Analysis of the CT number for high-density materials using 16-bit scanners will extend the CT scale that currently exists for 12-bit scanners and thus will be beneficial for use in CT–electron density (ED) curve in radiotherapy treatment planning system (TPS). Implementation of this extended CT scale will compensate for tissue heterogeneity during CT–ED conversion in treatment planning.Materials and methodsAn in-house built phantom with 10 different metal samples was scanned using 80, 100 and 120 kVp in two different CT scanners. A region of interest was set at the centre of the material and the mean CT numbers together with data deviation were determined. Dosimetry calculation was performed by applying a direct anterior beam on 12-bit, 12-bit extended and 16-bit.ResultsHigh-density materials (&amp;gt;4·34 g cm−3) in 16-bit depth provide disparities up to 44% compared to Siemens’ 12-bit extended. Influence of tube voltage showed a significant difference (p&amp;lt;0·05) in both bit depth and CT number of the gold and amalgam saturated in 16-bit depth. A 120 kVp energy illustrated a low variation on CT number for different scanners, but dosimetry calculation showed significant disparities at the metal interface in 12-bit, 12-bit extended and 16-bit.FindingsHigh-density materials require 16-bit scanners to obtain CT number to be implemented in treatment planning in radiotherapy. This also suggests that proper tube voltage together with correct CT–ED resulted in accurate TPS algorithm calculation.

Comparative SEM analysis of nine F22 aligner cleaning strategies

BackgroundThe orthodontics industry has paid great attention to the aesthetics of orthodontic appliances, seeking to make them as invisible as possible. There are several advantages to clear aligner systems, including aesthetics, comfort, chairside time reduction, and the fact that they can be removed for meals and oral hygiene procedures.MethodsFive patients were each given a series of F22 aligners, each to be worn for 14 days and nights, with the exception of meal and brushing times. Patients were instructed to clean each aligner using a prescribed strategy, and sections of the used aligners were observed under SEM. One grey-scale SEM image was saved per aligner in JPEG format with an 8-bit colour depth, and a total of 45 measurements on the grey scale (“Value” variable) were made. This dataset was analysed statistically via repeated measures ANOVA to determine the effect of each of the nine cleaning strategies in each of the five patients.ResultsA statistically significant difference in the efficacy of the cleaning strategies was detected. Specifically, rinsing with water alone was significantly less efficacious, and a combination of cationic detergent solution and ultrasonication was significantly more efficacious than the other methods (p < 0.05).ConclusionsOf the nine cleaning strategies examined, only that involving 5 min of ultrasonication at 42 k Hz combined with a 0.3% germicidal cationic detergent was observed to be statistically effective at removing the bacterial biofilm from the surface of F22 aligners.

Multi-ramp reference voltage PWM imaging scheme with enhanced dynamic range and correlated double sampling

A multi-ramp reference voltage readout scheme for CMOS digital pixel sensor (DPS) based on pulse width modulation (PWM) imaging is proposed to extend the dynamic range (DR) and eliminate the fixed pattern noise (FPN). The exposure period is divided into two sampling stages, the FPN and some random noise can be eliminated by time-domain correlated double sampling (CDS). The multiple ramps are introduced to obtain a larger DR although the integration time is limited. A behavioral model of the PWM DPS with this scheme is simulated with MATLAB, the DR, photoresponse and FPN are analyzed versus the design parameters. A DPS with the multi-ramp reference can achieve 103.06dB DR with CDS at 100fps, 16-bit memory depth and 1.8V power supply. And the DR can be further extended to 138.49dB with a single sampling at high light condition. FPN can drop to less than 1LSB after the CDS operation from 3.97% without CDS.

ERICA: an energy resolving photon counting readout ASIC for X-ray in-line cameras

We present ERICA (Energy Resolving Inline X-ray Camera) a photon-counting readout ASIC, with 6 energy bins. The ASIC is composed of a matrix of 8 × 20 pixels controlled by a global digital controller and biased with 7 independent digital to analog converters (DACs) and a band-gap current reference. The pixel analog front-end includes a charge sensitive amplifier with 16 mV/ke− gain and dynamic range of 45 ke−. ERICA has programmable pulse width, an adjustable constant current feedback resistor, a linear test pulse generator, and six discriminators with 6-bit local threshold adjustment. The pixel digital back-end includes the digital controller, 8 counters of 8-bit depth, half-full buffer flag for any of the 8 counters, a 74-bit shadow/shift register, a 74-bit configuration latch, and charge sharing compensation processing to perform the energy classification and counting operations of every detected photon in 1 μ s. The pixel size is 330 μm × 330 μm and its average consumption is 150 μW. Implemented in TSMC 0.25 μm CMOS process, the ASIC pixel's equivalent noise charge (ENC) is 90 e− RMS connected to a 1 mm thickness matching CdTe detector biased at −300 V with a total leakage current of 20 nA.

Series: Practical Evaluation of Clinical Image Quality (3): Subjective Evaluation of Image Quality in Digital Radiography Systems.

In the environment of picture archiving and communication system (PACS), the subjective evaluation of medical image quality is performed using an image display monitor. Commonly, PACS and its image viewer can handle digital imaging and communications in medicine (DICOM) format image files that are displayed as 8-bit grayscale images in the monitor. However, the original grayscale of DICOM image is between 10-bit to 16-bit depth. Therefore, in order for DICOM images to be displayed in the monitor by PACS image viewer, they need to be converted into 8-bit depth grayscale. This conversion indicates that the original characteristics of DICOM images are altered when they are displayed in the monitor by PACS image viewer. Moreover, image data interpolation to match the image matrix for the resolution of the monitor also alters the characteristics of images. For these reasons, it is a must to recognize the influential factors of image display settings for the subjective evaluation of medical image quality.