Images Of Human Body Research Articles

Retracted Article: Use of CBCT in Orthodontics- A Review

The article stands retracted due to double submission by the author Dr. Aakash Shah . It is quite regretful that such authors waste the time and resources of the journal. View the email sent by Dr. Aakash below. Lateral cephalometric radiographs are most commonly used as a diagnostic tool in orthognathic surgery as well as orthodontic treatment. But the limitation of lateral cephalograms is its 2 dimensional nature whereas the human body is 3 dimensional. Conventional 2D lateral cephalograms have numerous drawbacks in terms of investigating the changes in the alveolar bone and roots, particularly in the anterior region, as a consequence of the midsagittal projection. Additionally its accuracy is questionable as it has projection errors. The use of computed tomography in 3D imaging of human body is available in the field of medicine since last 30 years. CT scanning is the three dimensional imaging technique giving quantitative assessments of the buccal and lingual cortical bone plates and labiolingual width of alveolar bone with elevated accuracy and precision. But the use of computed tomography in dentistry is limited because the amount of radiation exposure with this technology is very high. Since the invention of Cone Beam Computed Tomography, the amount of radiation exposure in the patient is reduced. This enhances its use in obtaining the 3D images of the craniofacial structures. This technology helps in visualizing the hard and soft tissues of the craniofacial structures from various perspectives and helps in thorough diagnosis and treatment planning of orthognathic surgery and orthodontic patients. The principles of CBCT and its use in the field of orthodontics will be discussed in detail in this paper.

Commissioning of the J-PET Detector for Studies of Decays of Positronium Atoms

The Jagiellonian Positron Emission Tomograph (J-PET) is a detector for medical imaging of the whole human body as well as for physics studies involving detection of electron-positron annihilation into photons. J-PET has high angular and time resolution and allows for measurement of spin of the positronium and the momenta and polarization vectors of annihilation quanta. In this article, we present the potential of the J-PET system for background rejection in the decays of positronium atoms.

Mediated Compassion and the Politics of Displaced Bodies

In media's attempt to humanize the impact of war, dramatic images of disgruntled human bodies seeking refuge in faraway lands have become symbols of suffering and calls for humanitarian action. However, some media scholars are claiming that compassion fatigue is desensitizing media audiences to the human cost of war. This article argues that certain images resonate with greater signification that offers new ways of looking at compassion as a form of dialogue. The photo of Kim Phuc, the young Vietnamese girl burned by napalm on the fields of Vietnam stands out in our collective memory. There are many others but only a few attain such iconic status. And as war becomes more politically complex, so too are media?s role in portraying the suffering of children especially in the context of forced migration. The photographic image of the three-year old Syrian boy, Aylan Kurdi, washed ashore on a beach in Turkey and the many discourses it engendered represent a new mediated form of human suffering. As Barthes argued, there is a difference between a photograph that “shouts” and a photograph that “wounds”. The swift refugee policy changes expressed by some world leaders days after Aylan?s image came out in mass media is revealing on many levels. Drawing from online media coverage of this particular media event, this article argues that media's dialogic potential problematizes competing arguments between moral obligation and moral imperialism where culture and race are implicated in policy decisions on refugeehood and cultural integration.

Medical Image Compression using Lossless and Lossy Systems by 3 Dimensional Haar Wavelet Transform

Objectives: The advancement of digital structure human body images are created by Medical imaging. Compression of these images is hence needed for the images to be stored and transmitted. Compression of these images needs to be attained considerably, without compromising the image quality. Methods/Statistical Analysis: The most particular element of Haar Transform lies in the way that it lends itself effectively to fundamental manual estimations. It has been turned out to be an extremely constructive mechanism for image handling. The Haar convert reconverts a unique indication into Bi sub-level signals of quasi- its extent.3 Dimensional Haar Wavelet Transform (3 Dimensional HWT) is one of the computations which can moderate the estimation work in Haar Transform (HT) and Fast 3 Dimensional Haar Transform (3 Dimensional HT). Findings: The proposed work of 3 Dimensional HAAR WAVELET TRANSFORM (HWT) through ­parameterization cause to enhancement of efficiency in picture pressure with regards to unique choice of the wavelet and logarithmic DWT. Correlation between different lossy and lossless segments in 1−D image channels is ­investigated in this paper and a technique to make use of this redundancy is suggested. Application/Improvement: Any medical image compression application can adopt this technique and further improvements can be done on 3 Dimensional HAAR picture quality enhancement along with lossy compression.Keywords: (3 DimensionalHWT), 3 Dimensional Haar wavelet Transform, Image Compression, Lossless and Lossy Compression

7 T 자기공명영상시스템에서의 송수신 RF 공진기 최적화

Magnetic resonance imaging has a potential to produce clear anatomical as well as functional images of human body. However, the ability to diagnose is limited by signal to noise ratio (SNR) and the resolution of current medical systems. To remove the challenges prevalent due to the use of high field scanners, dedicated radio frequency coils are used. Transverse electromagnetic coils have an advantage of providing homogeneous magnetic field throughout the region but with low signal to noise ratio while surface coils have an advantage of providing higher signal to noise ratio but with low homogeneity. This research combines both the advantage into one by utilizing transmit only transverse electromagnetic radio frequency coils (8 channel) along with receive only surface coils (by varying the number) for better imaging of brain. A 7 Tesla 32-channel close fitting helmet shaped phased-array surface coils along with the combination of 8 channel transmit only transverse electromagnetic coils provided good homogeneity as well as significant SNR improvements throughout the human brain.

Improving Security Screening: A Comparison of Multistatic Radar Configurations for Human Body Imaging

Active millimeter-wave radar can be used for imaging concealed objects and detecting contraband at security checkpoints. Current state-of-theart systems are based on monostatic or quasimonostatic configurations that can misrepresent areas of the target when the specular reflection is oriented away from the incident direction. This article presents a fully multistatic millimeter-wave imaging architecture for human-body screening. Transmitters placed off the receiving aperture panel allow for maximizing the surface of the human body that can be reconstructed. A reduced number of transmitters allows for fast imaging, which minimizes the target motion effects. Multiple three-dimensional (3-D) simulation-based examples are presented to validate the proposed system.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

Compressively Sampled MRI Recovery Using Modified Iterative-Reweighted Least Square Method

Magnetic resonance imaging (MRI) is a medical imaging modality used for high-resolution soft-tissue imaging of human body. In traditional MRI acquisition methods, sampling is performed at Nyquist rate to store data in k-space. The MR image is recovered using inverse Fast Fourier Transform (FFT). This approach results in slow data acquisition process, which is uncomfortable for the patients. Compressed Sensing (CS) acquisition approach offers nearly perfect recovery of MR image using non-linear reconstruction algorithms even from partial k-space data. This study presents a novel method to reconstruct MR image from highly under-sampled data using modified Iterative-Reweighted Least Square (IRLS) method with additional data consistency constraints. IRLS is an effective numerical method used in convex optimization problems. The proposed algorithm was applied on original human brain and Shepp–Logan phantom image, and the data acquired from the MRI scanner at St. Mary’s Hospital, London. The experimental results show that the proposed algorithm outperforms Projection onto Convex Sets (POCS), Separable Surrogate Functional (SSF), Iterative-Reweighted Least Squares (IRLS), Zero Filling (ZF), and Low-Resolution (LR) methods based on the parameters, e.g. Peak Signal-to-Noise Ratio (PSNR), Improved Signal-to-Noise Ratio (ISNR), Fitness, Correlation, Structural SIMilarity (SSIM) index, and Artifact Power (AP).

MEMS based vacuum packaging method of optically readable infrared focal plane array

A MEMS based vacuum packaging method is developed for uncooled optically readable infrared focal plane array (OR-IRFPA). The vacuum packaging structure is made up of three layers: a glass visible window, a silicon spacer, and a silicon infrared window. The silicon spacer is bonded with the glass visible window to form two cavities: one cavity for getter and another one for OR-IRFPA. The isolation wall with micrometer scale pores is designed to ensure air's free flow between two cavities and prevent getter powder from harming pixels of OR-IRFPA. The infrared window is used as cover board and selectively transmitting 8---14 μm infrared radiation. Infrared images of human body are gotten by using the vacuum packaged OR-IRFPA, which shows the vacuum packaging method is feasible and useful for OR-IRFPA.

Implementation of K-means segmentation algorithm on Intel Xeon Phi and GPU: Application in medical imaging

The paper presents speed up of the k-means algorithm for image segmentation. This speed up is achieved by effective parallelization. For parallel implementation we focus on Many Integrated Core (MIC) architecture with Intel Xeon Phi coprocessors. The MIC implementation is compared with GPU, CPU and sequential implementation. To demonstrate parallel capabilities of k-means algorithm, segmentation of CT images of human body are used. Results of this work will be used for development of the software application for automatic 3D model reconstruction of heart and liver.

Bimaterial Cantilever Focal Plane Array for Uncooled Infrared Imaging Using Sandwich-Framed Structure

This paper reports a novel infrared focal plane array (FPA) using SiN x /SiO2/SiN x sandwiched structure as the supporting frames for bimaterial cantilever pixels. The device was fabricated by a bulk silicon process, where a trench backfill technique was employed to form the SiN x /SiO2/SiN x sandwiched frames. By replacing the silicon frame with SiN x /SiO2/SiN x sandwiched frame, the fill factor of the FPA is increased to 42%. Compared with other substrate-free FPAs with frames making of a thin layer of SiN x , the sandwiched frame provides more supporting strength for the cantilever pixels. Meanwhile, the reliability and the uniformity of the FPA were improved with a design of staggered arrangement of cantilever pixels. Thermal images of human bodies were captured successfully at room temperature. A complete analysis of device performance was also carried out both theoretically and experimentally, including thermomechanical sensitivity, noise equivalent temperature difference, and response time. [2015-0261]

Fall detection in dusky environment

Accidental fall is the most prominent factor that causes the accidental death of elder people due to their slow body reaction. Automatic fall detection technology integrated in a health-care system can assist human monitoring the occurrence of fall, especially in dusky environments. In this paper, a novel fall detection system focusing mainly on dusky environments is proposed. In dusky environments, the silhouette images of human bodies extracted from conventional CCD cameras are usually imperfect due to the abrupt change of illumination. Thus, our work adopts a thermal imager to detect human bodies. The proposed approach adopts a coarse-to-fine strategy. Firstly, the downward optical flow features are extracted from the thermal images to identify fall-like actions in the coarse stage. The horizontal projection of motion history images (MHI) extracted from fall-like actions are then designed to verify the incident by the proposed nearest neighbor feature line embedding (NNFLE) in the fine stage. Experimental results demonstrate that the proposed method can distinguish the fall incidents with high accuracy even in dusky environments and overlapping situations.

Lateral occipitotemporal cortex (LOTC) activity is greatest while viewing dance compared to visualization and movement: learning and expertise effects.

The lateral occipitotemporal cortex (LOTC) is comprised of subregions selectively activated by images of human bodies (extrastriate body area, EBA), objects (lateral occipital complex, LO), and motion (MT+). However, their role in motor imagery and movement processing is unclear, as are the influences of learning and expertise on its recruitment. The purpose of our study was to examine putative changes in LOTC activation during action processing following motor learning of novel choreography in professional ballet dancers. Subjects were scanned with functional magnetic resonance imaging up to four times over 34weeks and performed four tasks: viewing and visualizing a newly learned ballet dance, visualizing a dance that was not being learned, and movement of the foot. EBA, LO, and MT+ were activated most while viewing dance compared to visualization and movement. Significant increases in activation were observed over time in left LO only during visualization of the unlearned dance, and all subregions were activated bilaterally during the viewing task after 34weeks of performance, suggesting learning-induced plasticity. Finally, we provide novel evidence for modulation of EBA with dance experience during the motor task, with significant activation elicited in a comparison group of novice dancers only. These results provide a composite of LOTC activation during action processing of newly learned ballet choreography and movement of the foot. The role of these areas is confirmed as primarily subserving observation of complex sequences of whole-body movement, with new evidence for modification by experience and over the course of real world ballet learning.

Conductivity imaging of pseudo human body by the electrical exploration method

Conductivity imaging of pseudo human body by the electrical exploration method

Potential of the J-PET Detector for Studies of Discrete Symmetries in Decays of Positronium Atom --- A Purely Leptonic System

The Jagiellonian Positron Emission Tomograph (J-PET) was constructed as a prototype of the cost-effective scanner for the simultaneous metabolic imaging of the whole human body. Being optimized for the detection of photons from the electron-positron annihilation with high time- and high angular-resolution, it constitutes a multi-purpose detector providing new opportunities for studying the decays of positronium atoms. Positronium is the lightest purely leptonic object decaying into photons. As an atom bound by a central potential it is a parity eigenstate, and as an atom built out of an electron and an anti-electron it is an eigenstate of the charge conjugation operator. Therefore, the positronium is a unique laboratory to study discrete symmetries whose precision is limited in principle by the effects due to the weak interactions expected at the level of (~10$^{-14}$) and photon-photon interactions expected at the level of (~10$^{-9}$). The J-PET detector enables to perform tests of discrete symmetries in the leptonic sector via the determination of the expectation values of the discrete-symmetries-odd operators, which may be constructed from the spin of ortho-positronium atom and the momenta and polarization vectors of photons originating from its annihilation. In this article we present the potential of the J-PET detector to test the C, CP, T and CPT symmetries in the decays of positronium atoms.

J-PET detector system for studies of the electron-positron annihilations

Jagiellonian Positron Emission Tomograph (J-PET) has been recently constructed at the Jagiellonian University as a prototype of a cost-effective scanner for the metabolic imaging of the whole human body. J-PET detector is optimized for the measurement of momentum and polarization of photons from the electron-positron annihilations. It is built out of strips of plastic scintillators, forming three cylindrical layers. As detector of gamma quanta it will be used for studies of discrete symmetries and multiparticle entanglement of photons originating from the decays of ortho-positronium atoms.

Comparing 2D image features on viewpoint independence using 3D anthropometric dataset

We study the viewpoint-independence of image features in the classification of identities using multiple-view full-body images. A reliable vision system should be robust in classifying objects from images captured on novel viewpoints. To obtain a robust classifier, 3D models are collected for rendering training and testing images from various viewpoints. These images are then used for extracting features and building classifiers. In this work, we compute multiple view human-body images from a 3D anthropometry human body database. For each subject, a majority of the views are randomly selected to be included in the training dataset and the remaining views are used for testing. More specifically, we use histogram of oriented gradient (HOG) feature-based support vector machine (SVM) as the baseline to be compared with deep auto-encoders network and deep convolutional neural networks (CNN). Through experiments, we conclude that the deep CNN performs the best (deep auto-encoders network as the runner-up) in computing viewpointindependent image features for identity classifications based on 2D full-body images.

Comparing 2D image features on viewpoint independence using 3D anthropometric dataset

We study the viewpoint-independence of image features in the classification of identities using multiple-view full-body images. A reliable vision system should be robust in classifying objects from images captured on novel viewpoints. To obtain a robust classifier, 3D models are collected for rendering training and testing images from various viewpoints. These images are then used for extracting features and building classifiers. In this work, we compute multiple view human-body images from a 3D anthropometry human body database. For each subject, a majority of the views are randomly selected to be included in the training dataset and the remaining views are used for testing. More specifically, we use histogram of oriented gradient (HOG) feature-based support vector machine (SVM) as the baseline to be compared with deep auto-encoders network and deep convolutional neural networks (CNN). Through experiments, we conclude that the deep CNN performs the best (deep auto-encoders network as the runner-up) in computing viewpointindependent image features for identity classifications based on 2D full-body images.

The study of the anatomy of the urogenital system. From antiquity to the our days

The article describes the main stages of the study of the anatomy of the urinary system, as well as the evolution of the human body imaging. It presents the most significant discoveries in this area of outstanding scientists from antiquity to the our days in chronological order.

Continuous Gesture Recognition using HLAC and Low-Dimensional Space

This study proposes a continuous gesture recognition using information about features of motions obtained from continuous gesture images through higher order correlation feature coefficient and principal component analysis (PCA). The proposed method, first, separates two-dimensional silhouette gesture region from a continuous input image that includes a human body image. Information about 35 features are extracted using the higher order local auto correlation coefficient in the divided image and a low-dimensional gesture space is composed using PCA. The model feature value reflected in the gesture space is composed of symbols of certain conditions through clustering algorithm so as to be used as the input symbol of a hidden marker model, and a random input motion is recognized as the relevant gesture model with the highest probability value. The proposed method has less computation workload than the previous geometric feature-based method or appearance-based method and shows a high recognition rate with using the minimum information, so it is very suitable for the construction of a real-time system. In addition, the recognized gesture can be used input data of various applications like dynamic motion type game by mapping to an operating system. And the human motions can be used the objects of observation, such as smart home and behavior analysis in special spaces.