Construction of three-dimensional atlas of the lenticular nuclei and its subnucleus based on the cryosection images from Chinese visible human:a preliminary study

Objective To establish a 3D atlas of the lenticular nuclei and its subnucleus with the cryosection images of the male from "Atlas of Chinese Visible Human". Methods The lenticular nuclei and its subnucleus were segmented from the cryosection images and reconstructed with the software

Tracking of iron-labeled human neural stem cells by magnetic resonance imaging in cell replacement therapy for Parkinson＇s disease

Human neural stem cells（h NSCs） derived from the ventral mesencephalon are powerful research tools and candidates for cell therapies in Parkinson＇s disease. However, their clinical translation has not been fully realized due, in part, to the limited ability to track stem cell regional localization and survival over long periods of time after in vivo transplantation. Magnetic resonance imaging provides an excellent non-invasive method to study the fate of transplanted cells in vivo. For magnetic resonance imaging cell tracking, cells need to be labeled with a contrast agent, such as magnetic nanoparticles, at a concentration high enough to be easily detected by magnetic resonance imaging. Grafting of human neural stem cells labeled with magnetic nanoparticles allows cell tracking by magnetic resonance imaging without impairment of cell survival, proliferation, self-renewal, and multipotency. However, the results reviewed here suggest that in long term grafting, activated microglia and macrophages could contribute to magnetic resonance imaging signal by engulfing dead labeled cells or iron nanoparticles dispersed freely in the brain parenchyma over time.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Human Body Image Edge Detection Based on Wavelet Transform

Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

Towards in vivo photoacoustic human imaging:Shining a new light on clinical diagnostics

Multiscale visualization of human anatomical structures is revolutionizing clinical diagnosis and treatment.As one of the most promising clinical diagnostic techniques,photoacoustic imaging(PAI),or optoacoustic imaging,bridges the spatial-resolution gap between pure optical and ultrasonic imaging techniques,by the modes of optical illumination and acoustic detection.PAI can non-invasively capture multiple optical contrasts from the endogenous agents such as oxygenated/deoxygenated hemoglobin,lipid and melanin or a variety of exogenous specific biomarkers to reveal anatomy,function,and molecular for biological tissues in vivo,showing significant potential in clinical diagnostics.In 2001,the worldwide first clinical prototype of the photoacoustic system was used to screen breast cancer in vivo,which opened the prelude to photoacoustic clinical diagnostics.Over the past two decades,PAI has achieved monumental discoveries and applications in human imaging.Progress towards preclinical/clinical applications includes breast,skin,lymphatics,bowel,thyroid,ovarian,prostate,and brain imaging,etc.,and there is no doubt that PAI is opening new avenues to realize early diagnosis and precise treatment of human diseases.In this review,the breakthrough researches and key applications of photoacoustic human imaging in vivo are emphatically summarized,which demonstrates the technical superiorities and emerging applications of photoacoustic human imaging in clinical diagnostics,providing clinical translational orientations for the photoacoustic community and clinicians.The perspectives on potential improvements of photoacoustic human imaging are finally highlighted.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

A method for detecting miners based on helmets detection in underground coal mine videos

In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.

JMNet: A joint matting network for automatic human matting

We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human images.We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory costs.Our model can process real images downloaded from the Internet for use in composition applications.

Forensic Human Image Identification Using Medical Indicators

Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours and use the personal medical history for human identification.In this article,medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced,and were applied in the field of forensic identification of human images,which we called medical forensic identification of human images(mFIHI).The proposed method analysed the people’s medical signs by studying the appearance and behaviour characteristics depicted in images or videos,and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects.Through a conformity and difference analysis on medical indicators and their indicated diseases,it would provide an important information for human identification from images or videos.A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI,and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.

3D depth-coded photoacoustic microscopy with a large field of view for human skin imaging

Photoacoustic （PA） microscopy comes with high potential for human skin imaging, since it allows noninvasively high-resolution imaging of the natural hemoglobin at depths of several millimeters. Here, we developed a PA microscopy to achieve high-resolution, high-contrast, and large field of view imaging of skin. A three-dimensional （3D） depth-coding technology was used to encode the depth information in PA images, which is very intuitive for identifying the depth of blood vessels in a two-dimensional image, and the vascular structure can be analyzed at different depths. Imaging results demonstrate that the 3D depth-coded PA microscopy should be translated from the bench to the bedside.

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation,connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.