Improved Picture Quality Research Articles

REVOLUTIONIZING ONCOLOGY IMAGING: A PARADIGM SHIFT THROUGH STANDARDIZED MRI PROTOCOLS: VOLUME 2

Introduction: Oncology imaging is always developing, thus rapid and precise diagnosis are essential for cancer therapy. Recognizing the importance of Magnetic Resonance Imaging, our Mahamana Pandit Madan Mohan Malaviya Cancer Centre is standardizing MRI protocols to improve inspection and interpretation. This project aims to standardize, eliminate variance, and speed up patient care choices. Methods: Our standardized MRI procedures summarize scan parameters, sequences, modes, matrix and other data by body part. A 3 Tesla MRI machine was used to study how scanner differences affect parameters. Detailing appropriate pathological visualization sequences allowed diagnostic versatility. Localiser or scanogram was emphasized for exact scan plane alignment, ensuring uniform pictures for smooth comparisons. Results:Specific procedures were established for certain anatomical locations, with a focus on precise patient placement. Optimizing the localizer setup and selecting the appropriate coil can enhance the quality of images and increase diagnostic accuracy. Discussion: The conversation emphasizes the crucial need of standardizing MRI methods to ensure prompt and precise cancer diagnosis. The benefits of using this technology, such as maintaining consistency, saving time, and improving picture quality, help speed up the diagnostic process and lead to better outcomes for patients. Conclusion: Standardizing MRI procedures is a major step toward efficient and patient-centered cancer imaging, addressing varied oncological issues and improving consistency, time efficiency, and picture quality. Staff training and cooperation put our institution at the forefront of technology advances to improve cancer patient outcomes. We aim to enhance cancer care and treatment via cooperation, improvement, and a patient-centric approach.

Not All Flat-Panel C-Arms Are Created Equal: A Comparison of Three Major Manufacturers.

Introduction: Flat-panel detector C-arms (FCs) are reported to reduce radiation exposure and improve image quality compared with conventional image intensifier C-arms (CCs). The purpose of this study was to compare radiation exposure and image quality between three commonly used FCs. Materials and Methods: A cadaver model was placed in the prone position to simulate percutaneous nephrolithotomy. We compared the following three FCs: OEC Elite CFD from GE HealthCare, Zenition 70 from Philips, and Ziehm Vision RFD from Ziehm Imaging. To measure the radiation dose, optically stimulated luminescence dosimeters (OSLDs) were utilized during five 300-second trials, conducted under three settings: automatic exposure control (AEC), AEC with low dose (LD), and LD with the lowest pulse rate (LDLP). Ten blinded urologists evaluated the image quality. Data were statistically analyzed using the analysis of variance (ANOVA) and Tukey's B post hoc tests. Results: In the AEC setting, the Philips C-arm demonstrated lower ventral OSLD exposure (42,446 mrad) compared with both the GE (51,076 mrad) and Ziehm (83,178 mrad; p < 0.001) C-arms. Similarly, in the LD setting, the Philips C-arm resulted in less ventral OSLD exposure (25,926 mrad) than both the Ziehm (30,956 mrad) and GE (38,209 mrad; p < 0.001) C-arms. Meanwhile, in the LDLP setting, the Ziehm C-arm showed less ventral OSLD exposure (4019 mrad) than both the GE (7418 mrad) and Philips (8229 mrad; p < 0.001) C-arms. All three manufacturers received adequate image quality ratings at the AEC and LD settings. However, at LDLP, the Ziehm C-arm received inadequate ratings in 8% of images, whereas both the GE and Philips C-arms received 100% adequate ratings (p = 0.016). Conclusions: Radiation produced by flat-panel C-arms varies dramatically, with the highest exposure (Ziehm) being almost double the lowest (Philips) in AEC. Improved picture quality at the lowest settings may come at the cost of increased radiation dose. Surgeons should carefully select the machine and settings to minimize radiation exposure while still preserving the image quality.

A Method of Tactile Resistive Sensor Array Calibration

Resistive sensor arrays (RSA) comprising of pressure-sensitive elements find broad use in pressure-sensing applications. However, sensor manufacturing inevitably yields a number of non-ideal properties. The gain, offset or non-linearities of an RSA’s elements may vary between individual sensors (taxels). Hence a simple calibration procedure to suppress these effects is desirable. The paper presents a method of and apparatus for the calibration of a pressure-sensing RSA. A forward mathematical model for each sensor is obtained during the calibration phase. In subsequent online measurement, the inverse model is utilised to compensated for the non-uniformity in sensor gain and offset. This approach leads to an appreciable improvement in picture quality, which can be mathematically quantified using classification accuracy in our tactile anti-decubitus platform for human monitoring. Namely, in a four-class classification experiment involving a support vector machine, the classification error decreased from 11 % before calibration to 3.5 % after calibration. Owing to the calibration procedure, a classifier trained using a calibrated RSA can be deployed to another calibrated RSA, without further data collection.

Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.

Comparison of the outcomes of endoscopic versus microscopic approach in cholesteatoma surgery: a randomized clinical study

BackgroundEndoscopic ear surgery is a new, less invasive otologic procedure. Since 1990s, it has been grown in prominence because of anatomical and physiological theories. It offers a view of hidden places, avoids mastoidectomies and endaural vertical and postauricular incisions, and has many other benefits over microscopic surgery. The purpose of this study was to distinguish the difference between the outcomes of microscopic and endoscopic cholesteatoma surgery.MethodsEighty individuals who were diagnosed with cholesteatoma participated in this randomized clinical research. Patients were randomly allocated into two equivalent treatment groups. One group submitted to tympanoplasty via microscopic ear surgery and the other group had exclusive trans-canal endoscopic ear surgery. All patients had preoperative otomicroscopic and radiologic assessment to ensure cholesteatoma diagnosis. Audiological evaluations were also obtained.ResultsThere was no discernible difference in the tested groups’ operational times, air-bone gaps, or air conduction. The endoscopic surgery group healed significantly more quickly than the microscopic surgery group (5.4 0.5 vs 7.7 0.5 weeks, p > 0.001). The frequency of residual lesions (5.0% vs 22.5%, p = 0.023) and recurrence (7.5% vs 27.5%, p = 0.019) was significantly lower in the endoscopic group of patients.ConclusionsWhen comparing healing times, rates of residual disease and rates of recurrence, endoscopic surgery clearly outperforms microscopic surgery. The endoscope encourages visualization and magnification of anatomy while being less invasive thanks to its improved picture quality, lighting, and capacity to “see around the corner.” Additionally, a significant portion of viable mastoid tissues and healthy mucosa were preserved.

Deep Convolutional Attention based Bidirectional Recurrent Neural Network for Measuring Correlated Colour Temperature from RGB images

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively.

Field programmable gate array based moving object tracking system for robot navigation

This paper proposes a method in which an object tracking robot system is implemented on field programmable gate arrays (FPGAs). The OV7670 camera provides real-time object pictures to the system. To improve picture quality, images are put via the median filter phase. The item is distinguished from the backdrop based on color (red), after which it is subjected to a mathematical morphological approach of filtering to eliminate noise. To send the robot control signals, the object's (new) coordinates are found. In this method, the median filter, color separation, hardware IP cores, and morphological filter are all part of the embedded system on FPGA. Through the direct memory access (DMA) controller, these cores may communicate and perform high-speed pipeline computing at higher data rates. The entire system is executed in real-time on Xilinx's spartan-6 FPGA KIT. The results show practical and promise.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recognition and tracking. The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions, such as mist, fog, dust etc. The pictures then shift in intensity, colour, polarity and consistency. A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient environments. In recent years, target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities. However, the identification of objects in the night world also poses further problems because of the distorted backdrop and dim light. The Correlation aware LSTM based YOLO (You Look Only Once) classifier method for exact object recognition and determining its properties under night vision was a major inspiration for this work. In order to create virtual target sets similar to daily environments, we employ night images as inputs; and to obtain high enhanced image using histogram based enhancement and iterative wiener filter for removing the noise in the image. The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding (AILE) and uplift linear discriminant analysis (ULDA). The region of interest mask can be segmented using the Recurrent-Phase Level set Segmentation. Finally, we use deep convolution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based (LSTM) with YOLO method for object tracking system. A range of experimental findings demonstrate that our technique achieves high average accuracy with a precision of 99.7% for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism. Our approach may therefore satisfy the true demands of night scene target detection applications. We very much believe that our method will help future research.

P‐3.14: Development of Thin Film Design based on IGZO‐TFT Displays for Transmittance and Picture Quality Improvement

P‐3.14: Development of Thin Film Design based on IGZO‐TFT Displays for Transmittance and Picture Quality Improvement

36.3: A Dynamic Contrast Enhancement Method for TV Picture Quality Improvement

36.3: A Dynamic Contrast Enhancement Method for TV Picture Quality Improvement

P‐7.11: A Study on Local Dimming Algorithm Design for MINI LED Backlight Display Quality Improvement

Today HDR (High Dynamic Range) Displays are increasingly developed. The HDR Display with mini‐led backlight and liquid‐crystal panel plays an important role, it has a lot of advantages like high light (more than 1000nits), high contrast ratio (more than 100000:1), bright color and good permeability and so on. But it also has some display defects like halo[3] phenomenon in a dark scene. In this paper, first, we discuss some common picture quality issues caused by the normal local dimming algorithm[2] and then present a novel algorithm base on BOE COG (chip on glass) mini‐led backlight HDR display. The BOE COG technology is a global unique glass substrate backlight solution, and has some features to help the algorithm improve its performance. And for the local dimming display issues, the algorithm itself has room for optimizing visual effects. Finally, around the key point of local dimming algorithm, we show several advantages of BOE glass substrate backlight solution, its distinctive features can help the algorithm improve picture quality.

Experimental Analysis of Low-Light Image Enhancement Techniques: A Review

Images acquired in low-light situations generally suffer from subjective visual effects on human eyes and the performance of different machine vision systems due to low brightness, low contrast, a limited gray range, color distortion, and high noise. Enhancing photographs taken in low light is done so that they may be used in other applications more effectively. In this study, we look back at some of the most important methods for improving images in dim light. We categorize these techniques into seven distinct groups: gray transformation, histogram equalization, Retinex, frequency-domain, picture fusion, defogging model, and machine learning. Methods are presented across the board, from the broadest to the most specific categories, emphasizing the underlying concepts and distinguishing features. Different improved picture quality assessment techniques are also described, as are comparisons of the various algorithms used. Finally, a state-of-the-art summary and recommendations for where the field should go are presented.

Study of image fusion optimization techniques for medical applications

Image fusion is becoming increasingly important in computer vision activities because of the larger number of capture methods. The integration of different views like multi view, multi temporal view and information which is larger together termed as image fusion (IF). In image fusion the information of multiple-sensors are converted into a unified image while maintaining the fidelity of critical characteristics.Healthcare image fusion procedures are used to improve picture quality by achieving the conspicuous characteristics in the fusion findings. As a result, they increase the practical usefulness of medical pictures for issue assessment and identification. Fusion of medical images are generally evaluated using the modalities like MRI-Magnetic Resonance Imaging, MRA-Magnetic Resonance Angiogram, PET-Positron Emission Tomography, SPET-Structural Positron Emission Tomography,CT-Computed Tomography, and SPECT-Single-Photon Emission Computed Tomography. Neural network and optimization techniques help in improving the quality of fused image. In this paper various fusion techniques are studied. Along with different fuson approaches, the outcomes of diverse research projects are contrasted in terms of how the pulse coupled neural network is employed and different optimization techniques. The Pulse Couple Neural Networks (PCNN) using various optimization techniques are compared. Among which the swarming mechanism of salps improves the performance of the system. The PCNN is combined with SSO algorithm and evaluated the results.From the results it is shown that PCNN- Salp Swarm Optimization (SSO)ishaving good value of Peak Signal to Noise Ratio (PSNR) with 45.93 and Structural Similarity Index(SSIM) is 0.996.

Performance Optimization of 3-DOF Application Scene Based on 360-Degree Panoramic Technology with Depth Information

High-quality 3D scenes often show poor rendering effect and insufficient operational performance in low-end devices. Therefore, how to make better use of 360-degree panoramic technology and replace the traditional pure model scene by visual deception is of great significance to the picture quality improvement and performance optimization of low-end equipment. In this paper, a three-degree-of-freedom (3-DOF) application scenario for indoor simulation is used as an example. Under the premise of 360-degree panoramic technology, depth information is obtained through laser ranging, converted into a two-dimensional form of depth image with the help of spherical coordinates, and combined with a transparent information image, the depth image is compressed using DEFLATE compression algorithm, and finally be stored. By optimizing the data through bilinear sampling and with the help of geometric knowledge, the occluded parts are removed or translucent according to the actual situation when the graphics are rendered, and the occlusion relationship is correctly handled in order to achieve a real integration of the virtual environment with the 3D objects. Through multiple data, it is shown that the method possesses significant performance improvement while ensuring geometric realism.

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

Investigation of the digital technologies influence on the process transformation in cinema

Within the sectoral structure of the national economy, audiovisual industries are parts of the cultural industry. Cinema is a particularly effective means of satisfying the human need for affective self-expression and establishing emotional communication between people. To increase the audiovisual product competitiveness, an analysis of the digital technologies impact on it was carried out in order to attract an additional audience. It is noted that modern technologies have paved the way for multifunctional devices: the creative process is increasingly becoming technological in nature. The use of digital technologies has led to the creation of high-quality films. Due to technological progress, the equipment becomes available to more participants in the film business. Digital technologies have become a powerful and accessible tool that allows non-professional artists/directors to enter the cinematic space. However, for the author’s cinema, digital technologies have only become a tool to improve picture quality, as well as expand the capabilities for the artist / director to bring their ideas to life on screen.

589 Video-Enhanced Telepresence for Burn Care may Improve Patient and Staff Satisfaction

IntroductionTelemedicine and telepresence technology successfully contributes to the care of patients in different medical disciplines. Burn care is a highly specialized field that requires a multidisciplinary team and frequent visual evaluation of a patient’s injuries. Wound care can be extensive and demanding. Physician availability at the “Wound Care Room” is often limited by other responsibilities in the operating room, emergency department, surgical clinic, or academic conferences. We hypothesized that the incorporation of a telemedicine platform would provide greater access to surgical providers (SP- burn surgeons, general and plastic surgery residents) and allow for more efficient evaluation and prompt decision making. We also predicted that it would improve communication among SP, nursing staff (NS- nurses and nurse aides), and burn patients (BP) in real time without compromising BP privacy and comfort.MethodsA dual-way video and voice telemedicine platform was incorporated into burn care at a Level-1 Trauma Center and ABA-verified Burn Center. The video module was positioned so that SP were able to remotely assess the progression of burn injuries during wound care. Patients included were hospitalized and undergoing wound care by NS. Adult BP were included regardless of age, burn thickness, and burn surface area. BP, SP, and NS were asked the following questions after wound care had been provided:1. Did you feel comfortable using this technology?2. Was the patient’s sense of privacy compromised?3. Did use of the video module enhance the provision of care?4. Did use of the video module improve team communication?5. Overall, were you satisfied with the use of the platform?ResultsBP with ages ranging from 18-74 years old and with injuries involving 8-44% TBSA were included. Interviews from 38 patient encounters were conducted, and included input from 4 SP, 6 NS, and 24 BP.The BP, SP, and NS surveyed all reported comfort using this technology. There were no reports of concern for patient’s privacy. SP felt they could make a final management plan in 74% of the cases, with difficulty arising in 26% of cases due to image resolution. BP reported that use of the video modules contributed positively to their care in 87% of cases, with issues related to communication and lack of understanding arising in the other 13%.ConclusionsTelemedicine was well accepted by all the BP, SP, and NS. The perception from NS and SP was that it enhanced prompt communication contributing to better patient care. Final management decisions were achieved in most cases, with picture resolution being identified as an area for improvement. With improved picture quality, this technology can likely be used as a reliable decision-making tool to improve care.

An Integrated Scan Driver Circuit for Picture Quality and Uniformity Improvement of OLED Display

An Integrated Scan Driver Circuit for Picture Quality and Uniformity Improvement of OLED Display

P‐11.2: Novel Pixel Design for Transmittance and Picture Quality Improvement

A data‐line‐centered (DLC) pixel design is constructed to improve transmittance and picture quality. The interaction between the adjacent sub‐pixels prompts a superior LC alignment comparable to the conventional design. The narrowed cross dark fringe can contribute to the modification of skin color shift. Strategy for vertical cross‐talk risk is also presented and theoretically proven to be effective. Consequently, an expected improvement can be realized under the DLC design.

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.