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A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images
Full text linkThe medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for medical images. Secondly, the data is embedded into the lesion area by improved histogram shifting method to enhance the contrast of medial image’s lesion area. Lastly, the rest of data is embedded into the non-lesion area by the high-capacity embedding method to achieve the higher payload. At the receiving end, data can be extracted completely and images can be recovered losslessly by the third party with right. Experimental results have shown that the method of lesion extraction has an advantage over the existing segmentation methods in medical images. The image quality is improved well and the performance of contrast enhancement in the lesion area is better than other RDH methods with contrast enhancement
RETRACTED: Automatic Arrhythmia Detection Based on Convolutional Neural Networks
No full textECG signal is of great importance in the clinical diagnosis of various heart diseases. The abnormal origin or conduction of excitation is the electrophysiological mechanism leading to arrhythmia, but the type and frequency of arrhythmia is an important indicator reflecting the stability of cardiac electrical activity. In clinical practice, arrhythmic signals can be classified according to the origin of excitation, the frequency of excitation, or the transmission of excitation. Traditional heart disease diagnosis depends on doctors, and it is influenced by doctors' professional skills and the department's specialty. ECG signal has the characteristics of weak signal, low frequency, large variation, and easy to be interfered. In this investigation, an ECG anomaly automatic classification system based on the convolutional neural network is proposed. The training sets of the convolutional neural network are ECG beats extracted from the MIT-BIH database as training sets. A 36-layer convolutional neural network (CNN) is trained based on Caffe framework to classify ECG signals automatically. The experimental results show that it can reach or even exceed the level of a senior cardiologist in judging three diseases: FIB, AFL and IVR
Rigid Medical Image Registration Using Learning-Based Interest Points and Features
Full text linkFor image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy and interventional radiology, one of the important techniques is medical image registration. In our study, we propose a learning-based approach named “FIP-CNNF” for rigid registration of medical image. Firstly, the pixel-level interest points are computed by the full convolution network (FCN) with self-supervise. Secondly, feature detection, descriptor and matching are trained by convolution neural network (CNN). Thirdly, random sample consensus (Ransac) is used to filter outliers, and the transformation parameters are found with the most inliers by iteratively fitting transforms. In addition, we propose “TrFIP-CNNF” which uses transfer learning and fine-tuning to boost performance of FIP-CNNF. The experiment is done with the dataset of nasopharyngeal carcinoma which is collected from West China Hospital. For the CT-CT and MR-MR image registration, TrFIP-CNNF performs better than scale invariant feature transform (SIFT) and FIP-CNNF slightly. For the CT-MR image registration, the precision, recall and target registration error (TRE) of the TrFIP-CNNF are much better than those of SIFT and FIP-CNNF, and even several times better than those of SIFT. The promising results are achieved by TrFIP-CNNF especially in the multimodal medical image registration, which demonstrates that a feasible approach can be built to improve image registration by using FCN interest points and CNN features
A Review on Deep Learning Approaches to Image Classification and Object Segmentation
Full text linkDeep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Present proposed artificial neural networks and optimization skills have effectively achieved large-scale deep learnt neural networks showing better performance with deeper depth and wider width of networks. With the efforts in the present deep learning approaches, factors, e.g., network structures, training methods and training data sets are playing critical roles in improving the performance of networks. In this paper, deep learning models in recent years are summarized and compared with detailed discussion of several typical networks in the field of image classification, object detection and its segmentation. Most of the algorithms cited in this paper have been effectively recognized and utilized in the academia and industry. In addition to the innovation of deep learning algorithms and mechanisms, the construction of large-scale datasets and the development of corresponding tools in recent years have also been analyzed and depicted
Experimental Study of Aqueous Humor Flow in a Transparent Anterior Segment Phantom by Using PIV Technique
Full text linkPupillary block is considered as an important cause of primary angle-closure glaucoma (PACG). In order to investigate the effect of pupillary block on the hydrodynamics of aqueous humor (AH) in anterior chamber (AC) and potential risks, a 3D printed eye model was developed to mimic the AH flow driven by fluid generation, the differential pressure between AC and posterior chambers (PC) and pupillary block. Particle image velocimetry technology was applied to visualize flow distribution. The results demonstrated obvious differences in AH flow with and without pupillary block. Under the normal condition (without pupillary block), the flow filed of AH was nearly symmetric in the AC. The highest flow velocity located at the central of AC when the differential pressure between AC and PC was under 5.83 Pa, while it appeared near the cornea and iris surface when the differential pressure was greater than 33.6 Pa. Once pupillary block occurred, two asymmetric vortices with different sizes were observed and the shear stress in the paracentral cornea and iris epithelium increased greatly. It can be concluded that the pupillary block and the elevated differential pressure between AC and PC could change the flow distribution and thus increase the risk of corneal endothelial cells detachment. This study could make a further understanding of the pathogenesis of PACG
An Isogeometric Analysis Computational Platform for Material Transport Simulation in Complex Neurite Networks
Full text linkNeurons exhibit remarkably complex geometry in their neurite networks. So far, how materials are transported in the complex geometry for survival and function of neurons remains an unanswered question. Answering this question is fundamental to understanding the physiology and disease of neurons. Here, we have developed an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We modeled the transport process by reaction-diffusion-transport equations and represented geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solved the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solved the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Using our IGA solver, we simulated material transport in three basic models of the network geometry: a single neurite, a neurite bifurcation, and a neurite tree with three bifurcations. In addition, the robustness of our solver is illustrated by simulating material transport in three representative and complex neurite networks. From the simulation we discovered several spatial patterns of the transport process. Together, our simulation provides key insights into how material transport in neurite networks is mediated by their complex geometry
Traction Force Measurements of Human Aortic Smooth Muscle Cells Reveal a Motor-Clutch Behavior
Full text linkThe contractile behavior of smooth muscle cells (SMCs) in the aorta is an important determinant of growth, remodeling, and homeostasis. However, quantitative values of SMC basal tone have never been characterized precisely on individual SMCs. Therefore, to address this lack, we developed an in vitro technique based on Traction Force Microscopy (TFM). Aortic SMCs from a human lineage at low passages (4-7) were cultured 2 days in conditions promoting the development of their contractile apparatus and seeded on hydrogels of varying elastic modulus (1, 4, 12 and 25 kPa) with embedded fluorescent microspheres. After complete adhesion, SMCs were artificially detached from the gel by trypsin treatment. The microbeads movement was tracked and the deformation fields were processed with a mechanical model, assuming linear elasticity, isotropic material, plane strain, to extract the traction forces formerly applied by individual SMCs on the gel. Two major interesting and original observations about SMC traction forces were deduced from the obtained results: 1. they are variable but driven by cell dynamics and show an exponential distribution, with 40% to 80% of traction forces in the range 0-10 μN. 2. They depend on the substrate stiffness: the fraction of adhesion forces below 10 μN tend to decrease when the substrate stiffness increases, whereas the fraction of higher adhesion forces increases. As these two aspects of cell adhesion (variability and stiffness dependence) and the distribution of their traction forces can be predicted by the probabilistic motor-clutch model, we conclude that this model could be applied to SMCs. Further studies will consider stimulated contractility and primary culture of cells extracted from aneurysmal human aortic tissue
Oct-1 Mediates ACTH-Induced Proliferation of Vascular Smooth Muscle Cells
Full text linkAdrenocorticotrophic hormone (ACTH), a 39-amino acid peptide hormone, has been reported in the appreciation of the proliferation of vascular smooth muscle cells (VSMCs), however, the mechanism in molecular scale supporting the appreciation remains to be elucidated. In this study, we observed that the protein expression levels of ACTH at 24 h after exposure to 15% cyclic stretch were significantly higher than that after 5% cyclic stretch. When VSMCs were treated with 1000 nM ACTH directly, Oct-1 and lamin B1 expression were both up-regulated associating with each other, and the presence of Oct-1 was found shuttling between the cytosol and nucleus. When we silenced Oct-1 expression with RNA interference, the proliferation of VSMCs decreases significantly, which also validates a dominant contribution of Oct-1 in ACTH-induced VSMC proliferation. We further screened the target molecules of Oct-1 related to the proliferation with ingenuity pathway analysis (IPA), and found that superoxide dismutase 1 (SOD1) was significantly induced by ACTH stimulation yet suppressed by Oct-1 interference. All these findings in the present study highlight a new molecular mechanism that ACTH up-regulates Oct-1 expression and activates the protein expression of downstream target SOD1, finally induces the VSMC proliferation. The present work proved Octamer transcription factor-1 (Oct-1) as a key transcription factor in the mechanical regulation of VSMC proliferation, which in turn, provide a new target for the treatment of hypertension
System Identification of Heritage Structures Through AVT and OMA: A Review
Full text linkIn this review article, the past investigations carried out on heritage structures using Ambient Vibration Test (AVT) and Operational Modal Analysis (OMA) for system identification (determination of dynamic properties like frequency, mode shape and damping ratios) and associated applications are summarized. A total of 68 major research studies on heritage structures around the world that are available in literature are surveyed for this purpose. At first, field investigations carried out on heritage structures prior to conducting AVT are explained in detail. Next, specifications of accelerometers, location of accelerometers and optimization of accelerometer networks have been elaborated with respect to the geometry of the heritage structures. In addition to this, ambient vibration loads and data acquisition procedures are also discussed. Further, the state of art of performing OMA techniques for heritage structures is explained briefly. Furthermore, various applications of system identification for heritage structures are documented. Finally, conclusions are made towards errorless system identification of heritage structures through AVT and OMA
