KEYWORDS: Visual analytics, Feature extraction, Distortion, Image processing, Steganography, System on a chip, Error control coding, Image quality, Steganalysis, Visualization
Steganography is a technique applied to ensure secure communication. It is challenged by visual observation or statistical analysis to ascertain whether a message is hidden within a cover medium. Several steganographic methods have been proposed for palette-based images. These methods maintain image quality but cannot resist some statistical and visual attacks. To overcome this problem, two parity assignments with performances similar to those of existing parity assignments are proposed. An innovative embedding process that randomly selects one parity assignment while embedding secret bits in each pixel is provided. Finally, a steganographic method using the proposed embedding process and a specified adaptive scheme is presented. Experimental results revealed that the proposed method is undetectable under some statistical and visual attacks even as it maintains image quality.
The purpose of this paper is to design and implement an efficient iterative reconstruction algorithm for computational
tomography. We accelerate the reconstruction speed of algebraic reconstruction technique (ART), an
iterative reconstruction method, by using the result of filtered backprojection (FBP), a wide used algorithm of
analytical reconstruction, to be an initial guess and the reference for the first iteration and each back projection
stage respectively. Both two improvements can reduce the error between the forward projection of each iteration
and the measurements. We use three methods of quantitative analysis, root-mean-square error (RMSE), peak
signal to noise ratio (PSNR), and structural content (SC), to show that our method can reduce the number of
iterations by more than half and the quality of the result is better than the original ART.
This paper presents a method of X-ray image acquisition for the high-resolution tomography reconstruction
that uses a light source of synchrotron radiation to reconstruct a three-dimensional tomographic volume dataset
for a nanoscale object. For large objects, because of the limited field-of-view, a projection image of an object
should to be taken by several shots from different locations, and using an image stitching method to combine
these image blocks together. In this study, the overlap of image blocks should be small because our light source
is the synchrotron radiation and the X-ray dosage should be minimized as possible. We use the properties of
synchrotron radiation to enable the image stitching and alignment success when the overlaps between adjacent
image blocks are small. In this study, the size of overlaps can reach to 15% of the size of each image block. During
the reconstruction, the mechanical stability should be considered because it leads the misalignment problem in
tomography. We adopt the feature-based alignment
Fourier volume rendering (FVR) is a volume rendering method based on the Fourier slice theorem. With an n × n × n volume data, the FVR algorithm requires O(n2 log n) time to generate a result. Because it requires time less than O(n3) does, FVR is preferred for designing a real-time rendering algorithm with a preprocessing step. We improve upon our previous work. We demonstrate that a B-spline is significantly more useful when designing a transfer function. To design an appropriate transfer function with a spline function, additional control points are required. However, the memory space required for the proposed method increases in linear proportion to the number of control points. We show that the set of control points can be clustered into groups, ensuring the memory required is linearly proportional to the number of groups. The proposed technique supports real-time rendering after adjusting the transfer function for FVR.
Volume rendering is a technique for volume visualization. Given a set of N × N × N volume data, the traditional volume
rendering methods generally need O(N3) rendering time. The FVR (Fourier Volume Rendering), that takes advantage
of the Fourier slice theorem, takes O(N2log N) rendering time once the Fourier Transform of the volume data is
available. Thus the FVR is favor to designing a real-time rendering algorithm with a preprocessing step. But the FVR has
a disadvantage that resampling in the frequency domain causes artifacts in the spatial domain. Another problem is that
the method for designing a transfer function is not obvious. In this paper, we report that by using the spatial domain zero-padding
and tri-linear filtering can reduce the artifacts to an acceptable rendered image quality in spatial domain. To
design the transfer function, we present a method that the user can define a transfer function by using a Bezier curve
first. Based on the linear combination property of the Fourier transform and Bezier curve equation, the volume rendered
result can be obtained by adding the weighted frequency domain signals. That mean, once a transfer function is given,
we don't have to recompute the Fourier transform of the volume data after the transfer function applied. This technique
makes real-time adjustment of transfer function possible.
Expression of human Aβ42 peptide in the Drosophila brain induces pathological phenotypes resembling Alzheimer's disease (PNAS 101, 6623-6628). Three-dimensional confocal imaging reveals extensive vacuoles caused by neurodegeneration in the brain of aged but not young Aβ42 flies. Here, we report a three-dimensional computation algorism allowing semi-automatic measurement of numbers and volumes of brain vacuoles. The method employed matched filters, α-shape, and the active-contour techniques. Using this method, a good result depicting the contours of the vacuoles can be obtained. A more accurate algorithm is still under development. Accurate evaluation of brain pathology in Alzheimer's flies may facilitate the understanding of molecular mechanisms underlying Aβ toxicity and the discovery of novel therapeutic targets for Alzheimer's disease.
The ELISA (enzyme-linked immunosorbent assay) spot assay is a method widely used by immunologists to enumerate cytokine-producing cells within a specific cell population. The ELISA results are presented in an image containing numerous colored spots. We present a method to identify the spots in the image and report on important statistics regarding them. The proposed method employs color analysis in the CIE L*u*v* color space and matched filter technique. The system is trained to obtain a standard color for the spots and calculate the color differences between the spots and background in the L*u*v* space. Matched filters are then used to remove noise and enhance the spots in the color difference map. Intensity thresholding is applied to obtain a binary image in which the pixels in the spots have a grayscale of 1 while the grayscale of the other pixels is 0. A software system is implemented, based on this method, to help immunologists analyze the results obtained from the ELISA.
Finding the centerline of the tubular structure helps to segment or analyze the organs such as the vessels or neuron fibers in medical images. This paper described a semi-automatic method using the minimum cost path finding and Hessian matrix analysis in scale space to calculate the centerline of tubular structure organs. Unlike
previous approaches, exhaustive search for line-like shapes in every scale is prevent. Centerline pixels candidates and the width of the vessel are extracted by analyzing the intensity profile along the gradient vectors in the image. A verification procedure using Hassian matrix analysis with the scale obtained from the gradient analysis is applied to those candidates. Results obtained from the Hessian matrix analysis are used to construct a weighted graph. Finding the minimum cost path in the graph gives the centerline of the tubular structure. The method is applied to find the centerline of the vessels in the 2D angiogram and the neuron fibers in the 3D confocal microscopic images.
Accurate 3-D rectangular meshes construction of pipes is important and valuable for Engineering and Medicine to analyze the fluid mechanics. Hypertrophied prostate is suffered common by aged patient. Medicine trusts that the pathogenic reason can be interpreted using the statistics of analysis of fluid mechanics. We use serials of methods to construct the 3-D rectangular meshes of a patient urethra CT volumetric data given from medicine, and for mechanic engineering to gather statistics.
Confocal microscopy is an important tool in neural science research. Using proper staining technique, the neural network can be visualized in the confocal microscopic images. It is a great help if neural scientists can directly visualize the 3D neural network. Volume render the neuron fibers is not easy since other objects such as neuropils are also polluted in the staining process and the neuron fiber is thin comparing to the background. Preprocessing of the image to enhance the neuron fibers before volume rendering can help to build a better 3D image of the neural network. In this study, we used the Fourier Transform, the Wavelet Transform, and the matched filter techniques to enhance the neural fibers before volume rendering is applied. Experimental results show that such preprocessing steps help to generate a more clear 3D images of the neural network.
Accurate analysis of insect brain structures in digital confocal microscopic images is valuable and important to biology research needs. The first step is to segment meaningful structures from images. Active contour model, known as snakes, is widely used for segmentation of medical images. A new class of active contour model called gradient vector flow snake has been introduced in 1998 to overcome some critical problems encountered in the traditional snake. In this paper, we use gradient vector flow snake to segment the mushroom body and the central body from the confocal microscopic insect brain images. First, an edge map is created from images by some edge filters. Second, a gradient vector flow field is calculated from the edge map using a computational diffusion process. Finally, a traditional snake deformation process starts until it reaches a stable configuration. User interface is also provided here, allowing users to edit the snake during deformation process, if desired. Using the gradient vector flow snake as the main segmentation method and assist with user interface, we can properly segment the confocal microscopic insect brain image for most of the cases. The identified mushroom and central body can then be used as the preliminary results toward a 3-D reconstruction process for further biology researches.
KEYWORDS: Arteries, Computed tomography, 3D modeling, 3D image reconstruction, 3D image processing, Heart, Information science, Medical imaging, Medicine, Image resolution
We present a method to construct the geometric model of the pulmonary arteries from a set of cardiac CT scan images. It is desired that the model is in rectangular meshes. The main difficulties in this work are insufficient resolution along z-direction, the requirement of the rectangular meshes, and the geometric shape of the pulmonary arteries. We present a method that is based on estimation the medial axis and the radii of the vessel along the axis. We evaluate the proposed method using a phantom data set. The proposed method can achieve good reconstructed result for the phantom data set.
Accurate reconstruction of the human brain in MRI-T1 images is valuable and important to clinical needs. In this paper, the morphology and snake techniques are proposed to reconstruct a human brain model. First step in our method is to preprocess the volumetric image to remove skull, muscle, fat, and other non-brain tissue. We use a method of 3-d region growing. It has the advantage over thresholding that the resulting objects will be spatially connected, since brain has the connected property. Second, we use clustering method, and than use them to produce an initial estimate of the cortical surface. Third, we propose a novel active contour algorithm to move the snake toward the cortex. Thus we can use the snake to segment the brain. We use a wavelet method to model the external force that significantly increases the capture range of a traditional snake. Afterwards, we render the volumetric image to display the brain from multiple views. Both simulated data and patient data have been use to test the proposed techniques. The proposed method combines various techniques of 3-D morphology, clustering, active contour, wavelet, and volume rendering to accurately, robustly, and automatically reconstruct brain from MRI-T1 images.
We present a web-based collaborated diagnosis system developed using Java programming language. The system allows more than two physicians to look at the same images, to discuss through a chat box, and to make diagnosis collaboratively. The system provides tools such as window and value setting that physicians generally need. Beside the tools, we are implementing different tools for the system such as a volume rendering algorithm and an animation playback method. To make the system easily accessed, all the physician need is a web browser.
A method was developed for extraction coronary arteries from a contiguous sequence of angiographic images. Since coronary artery in the image usually has poor local contrast and has ribs, spine, and other tissues in the background. We remove the background using the information of temporal continuity. A set of multi-size matched filters process to enhance vessels from poor local contrast. The wavelet transformation based method is then employed to remove noise to enhance the image quality. We also design a stencil mask to remove the stationary tissues further.
Echocardiography is the most convenient means for both physicians and patients for heart disease diagnosis. The 3D + 1D echocardiogram provides important information for evaluation of the 3D heart function such as the ejection fraction or wall motion. The most basic task to evaluate such functions of a heart is to segment left ventricles and reconstruct the 3D geometric model of left ventricle from a set of echocardiographic images. Since there are many images involved, the method should not need too many user assists. In this work, we design a method for reconstructing the left ventricles with very few user assists.
Cardiac boundary extraction on echocardiographic images is essential for quantification of cardiac function. Tracing the endocardial boundaries on the end-diastolic and end-systolic images allows the computation of clinically important measures such as ejection rate. It is a clinical need for automatically detecting the borders. In this paper, we proposed a new approach for cardiac boundary extraction on echocardiographic images by directed graph. In this approach, we spread the cardiac image in the circular direction. The spread image is mapping to a directed graph. The shortest path is found by the dynamic programing algorithm. From the implemented results, we can obtain pretty good approximation for cardiac boundary extraction.
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