Recently, image encryption has been significantly improved by methods originating from optics. Phase retrieval is one method potentially applicable to image encryption. It is the process of algorithmically finding solutions to a phase loss problem due to light detectors only capturing the intensity of lights. Because the phase retrieval can constrain information to the phase with a specified condition, we propose using it to encrypt an image and compress it as phase information simultaneously. Moreover, the encryption will involve a customized key known only to authorized users. The encryption strategy provides dual protection of the encrypted image from unauthorized access. Experiments have been conducted to demonstrate the performance of the encryption method.
Brain functional activity involves complex cellular, metabolic, and vascular chain reactions, making it difficult to comprehend. Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) have been combined into a multimodal neuroimaging method that captures both electrophysiological and hemodynamic information to explore the spatiotemporal characteristics of brain activity. Because of the significance of visually evoked functional activity in clinical applications, numerous studies have explored the amplitude of the visual evoked potential (VEP) to clarify its relationship with the hemodynamic response. However, relatively few studies have investigated the influence of latency, which has been frequently used to diagnose visual diseases, on the hemodynamic response. Moreover, because the latency and the amplitude of VEPs have different roles in coding visual information, investigating the relationship between latency and the hemodynamic response should be helpful. In this study, checkerboard reversal tasks with graded contrasts were used to evoke visual functional activity. Both EEG and fNIRS were employed to investigate the relationship between neuronal electrophysiological activities and the hemodynamic responses. The VEP amplitudes were linearly correlated with the hemodynamic response, but the VEP latency showed a negative linear correlation with the hemodynamic response.
Multiple image encryption (MIE) was proposed to increase the efficiency of encrypting images by processing several images simultaneously. Because of the advantage of optical technology in processing twodimensional images at high throughput, MIE has been significantly improved by use of methods originating from optics. Phase retrieval was the process of algorithmically finding solutions to the phase loss problem due to light detectors only capturing the intensity. It was to retrieve phase information for the determination of a structure from diffraction data. Error-reduction algorithm is a typical phase retrieval method. Here, we employ it to illustrate that methods in phase retrieval are able to encrypt multiple images and compress them into encrypted data simultaneously. Moreover, the decryption is also designed to handle multiple images at the same time. The whole process including both the encryption and decryption is proposed to improve MIE with respect to the compression and efficiency. The feasibility and encryption of the MIE scheme is demonstrated with encryption experiments under Gaussian white noise and unauthorized access.
Cerebral oximeters measure continuous cerebral oxygen saturation using near-infrared spectroscopy (NIRS) technology noninvasively. It has been involved into operating room setting to monitor oxygenation within patient’s brain when surgeons are concerned that a patient’s levels might drop. Recently, cerebral oxygen saturation has also been related with chronic cerebral vascular insufficiency (CCVI). Patients with CCVI would be benefited if there would be a wearable system to measure their cerebral oxygen saturation in need. However, there has yet to be a wearable wireless cerebral oximeter to measure the saturation in 24 hours. So we proposed to develop the wearable wireless cerebral oximeter. The mechanism of the system follows the NIRS technology. Emitted light at wavelengths of 740nm and 860nm are sent from the light source penetrating the skull and cerebrum, and the light detector(s) receives the light not absorbed during the light pathway through the skull and cerebrum. The amount of oxygen absorbed within the brain is the difference between the amount of light sent out and received by the probe, which can be used to calculate the percentage of oxygen saturation.
In the system, it has one source and four detectors. The source, located in the middle of forehead, can emit two near infrared light, 740nm and 860nm. Two detectors are arranged in one side in 2 centimeters and 3 centimeters from the source. Their measurements are used to calculate the saturation in the cerebral cortex. The system has included the rechargeable lithium battery and Bluetooth smart wireless micro-computer unit.
KEYWORDS: Hemodynamics, Brain, Computing systems, Demodulation, Sensors, Near infrared, Signal detection, Channel projecting optics, Near infrared spectroscopy, Signal generators
Abundant study on the hemodynamic response of a brain have brought quite a few advances in technologies of measuring it. The most benefitted is the functional near infrared spectroscope (fNIRS). A variety of devices have been developed for different applications. Because portable fNIRS systems were more competent to measure responses either of special subjects or in natural environment, several kinds of portable fNIRS systems have been reported. However, they all required a computer for receiving data. The extra computer increases the cost of a fNIRS system. What’s more noticeable is the space required to locate the computer even for a portable system. It will discount the portability of the fNIRS system. So we designed a self-contained eight channel fNIRS system, which does not demand a computer to receive data and display data in a monitor. Instead, the system is centered by an ARM core CPU, which takes charge in organizing data and saving data, and then displays data on a touch screen. The system has also been validated by experiments on phantoms and on subjects in tasks.
Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.
Functional near infrared spectroscopy (fNIRS) is an optical technique measuring hemoglobin oxygenation and
deoxygenation concentrations of the brain cortex with higher temporal resolution than current alternative techniques. The
high temporal resolution enables collecting abundant brain functional information. However, the information collected
by fNIRS is correlated and mixed with a variety of physiological signals. Due to the mixture effect, activation detection
is one of challenges in fNIRS based studies of the brain functional activities. To achieve a better detection of activated
brain regions from the complicated information measures, we present a multi-scale analysis method based on a wavelet
coherence measure. In particular, the paradigm of an experiment is used as the reference signal. The coherence of the
signal with data measured by fNIRS at each channel is calculated and summed up to evaluate the activation level.
Experiments on simulated and real data have demonstrated that the proposed method is efficient and effective to detect
activated brain regions covered by the fNIRS probe.
In the area of computer-aided diagnosis (CAD), segmentation and analysis of hepatic vessel is a prerequisite for
hepatic diseases diagnosis and surgery planning. For liver surgery planning, it is crucial to provide the surgeon
with a patient-individual three-dimensional representation of the liver along with its vasculature and lesions.
The representation allows an exploration of the vascular anatomy and the measurement of vessel diameters,
following by intra-patient registration, as well as the analysis of the shape and volume of vascular territories.
In this paper, we present an approach for generation of hepatic vessel graph based on centerline extraction
and junction detection. The proposed approach involves the following concepts and methods: 1) Flux driven
automatic centerline extraction; 2) Junction detection on the centerline using hollow sphere filtering; 3) Graph
representation of hepatic vessel based on the centerline and junction. The approach is evaluated on contrast-enhanced
liver CT datasets to demonstrate its availability and effectiveness.
In this paper, we propose a novel approach for multiple surfaces segmentation based on the incorporation of physical
constraints in optimal surface searching. We apply our new approach to solve the renal cortex segmentation problem, an
important but not sufficiently researched issue. In this study, in order to better restrain the intensity proximity of the renal
cortex and renal column, we extend the optimal surface search approach to allow for varying sampling distance and
physical separation constraints, instead of the traditional fixed sampling distance and numerical separation constraints. The
sampling distance of each vertex-column is computed according to the sparsity of the local triangular mesh. Then the
physical constraint learned from a priori renal cortex thickness is applied to the inter-surface arcs as the separation
constraints. Appropriate varying sampling distance and separation constraints were learnt from 6 clinical CT images. After
training, the proposed approach was tested on a test set of 10 images. The manual segmentation of renal cortex was used as
the reference standard. Quantitative analysis of the segmented renal cortex indicates that overall segmentation accuracy
was increased after introducing the varying sampling distance and physical separation constraints (the average true positive
volume fraction (TPVF) and false positive volume fraction (FPVF) were 83.96% and 2.80%, respectively, by using
varying sampling distance and physical separation constraints compared to 74.10% and 0.18%, respectively, by using fixed
sampling distance and numerical separation constraints). The experimental results demonstrated the effectiveness of the
proposed approach.
The optical imaging takes advantage of coherent optics and has promoted the development of visualization of biological
application. Based on the temporal coherence, optical coherence tomography can deliver three-dimensional optical
images with superior resolutions, but the axial and lateral scanning is a time-consuming process. Optical scanning
holography (OSH) is a spatial coherence technique which integrates three-dimensional object into a two-dimensional
hologram through a two-dimensional optical scanning raster. The advantages of high lateral resolution and fast image
acquisition offer it a great potential application in three-dimensional optical imaging, but the prerequisite is the accurate
and practical reconstruction algorithm. Conventional method was first adopted to reconstruct sectional images and
obtained fine results, but some drawbacks restricted its practicality. An optimization method based on 2 l norm obtained
more accurate results than that of the conventional methods, but the intrinsic smooth of 2 l norm blurs the reconstruction
results. In this paper, a hard-threshold based sparse inverse imaging algorithm is proposed to improve the sectional image
reconstruction. The proposed method is characterized by hard-threshold based iterating with shrinkage threshold strategy,
which only involves lightweight vector operations and matrix-vector multiplication. The performance of the proposed
method has been validated by real experiment, which demonstrated great improvement on reconstruction accuracy at
appropriate computational cost.
KEYWORDS: Bioluminescence, Bone, Finite element methods, Tomography, Tissues, Chemiluminescence, Molecular imaging, Reconstruction algorithms, 3D image processing, Chemical elements
Among many optical molecular imaging modalities, bioluminescence imaging (BLI) has more and
more wide application in tumor detection and evaluation of pharmacodynamics, toxicity,
pharmacokinetics because of its noninvasive molecular and cellular level detection ability, high
sensitivity and low cost in comparison with other imaging technologies. However, BLI can not present
the accurate location and intensity of the inner bioluminescence sources such as in the bone, liver or
lung etc. Bioluminescent tomography (BLT) shows its advantage in determining the bioluminescence
source distribution inside a small animal or phantom. Considering the deficiency of two-dimensional
imaging modality, we developed three-dimensional tomography to reconstruct the information of the
bioluminescence source distribution in transgenic mOC-Luc mice bone with the boundary measured
data. In this paper, to study the osteocalcin (OC) accumulation in transgenic mOC-Luc mice bone, a
BLT reconstruction method based on multilevel adaptive finite element (FEM) algorithm was used for
localizing and quantifying multi bioluminescence sources. Optical and anatomical information of the
tissues are incorporated as a priori knowledge in this method, which can reduce the ill-posedness of
BLT. The data was acquired by the dual modality BLT and Micro CT prototype system that was
developed by us. Through temperature control and absolute intensity calibration, a relative accurate
intensity can be calculated. The location of the OC accumulation was reconstructed, which was
coherent with the principle of bone differentiation. This result also was testified by ex vivo experiment
in the black 96-plate well using the BLI system and the chemiluminescence apparatus.
Medical image segmentation is a prerequisite for visualization and diagnosis. State-of-the-art techniques of image
segmentation concentrate on interactive methods which are more robust than automatic techniques and more
efficient than manual delineation. In this paper, we present an interactive segmentation method for medical
images which relates to graph cut based on Support Vector Machines (SVMs). The proposed method is a
hybrid method that combines three aspects. First, the user selects seed points to paint object and background
using a "brush", and then the labeled pixels/voxels data including intensity value and gradient of the sampled
points are used as training set for SVMs training process. Second, the trained SVMs model is employed to
predict the probability of which classifications each unlabeled pixel/voxel belongs to. Third, unlike traditional
Gaussian Mixture Model (GMM) definition for region properties in graph cut method, negative log-likelihood
of the obtained probability of each pixel/voxel from SVMs model is used to define t-links in graph cut method
and the classical max-flow/min-cut algorithm is applied to minimize the energy function. Finally, the proposed
method is applied in 2D and 3D medical image segmentation. The experiment results demonstrate availability
and effectiveness of the proposed method.
Thermal ablation has been proved safe and effective as the treatment for liver tumors that are not suitable for
resection. Currently, manually performed thermal ablation is greatly dependent on the surgeon's acupuncture
manipulation against hand tremor. Besides that, inaccurate or inappropriate placement of the applicator will
also directly decrease the final treatment effect. In order to reduce the influence of hand tremor, and provide an
accurate and appropriate guidance for a better treatment, we develop an ultrasound-directed robotic system for
thermal ablation of liver tumors. In this paper, we will give a brief preliminary report of our system. Especially,
three innovative techniques are proposed to solve the critical problems in our system: accurate ultrasound calibration
when met with artifacts, realtime reconstruction with visualization using Graphic Processing Unit (GPU)
acceleration and 2D-3D ultrasound image registration. To reduce the error of point extraction with artifacts, we
propose a novel point extraction method by minimizing an error function which is defined based on the geometric
property of our N-fiducial phantom. Then realtime reconstruction with visualization using GPU acceleration is
provided for fast 3D ultrasound volume acquisition with dynamic display of reconstruction progress. After that,
coarse 2D-3D ultrasound image registration is performed based on landmark points correspondences, followed by
accurate 2D-3D ultrasound image registration based on Euclidean distance transform (EDT). The effectiveness
of our proposed techniques is demonstrated in phantom experiments.
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