Multi Images Research Articles

Using the self-synchronizing method to improve security of the multi chaotic systems-based image encryption

In this paper, we analyze the problems of vulnerability in the original multi chaotic systems-based image encryption scheme proposed by Huang and Nien [Optics Communications 282 (2009) 2123–2127]. A self-synchronizing method is proposed as enhancement measures to solve the problems and defeat cryptanalysis. Theoretical analysis and computer simulation indicate that the proposed encryption scheme is much more secure than the original one. At the same time, it holds the merits of the original scheme.

MR imaging of the knee in patients with medial unicompartmental arthroplasty: comparison among sequences at 1.5 T

This study was done to test a series of magnetic resonance (MR) imaging sequences of the knee after medial unicompartmental arthroplasty. Four patients who had undergone Oxford III medial unicompartmental arthroplasty underwent 1.5-T MR imaging of the operated knee using coronal sequences: T1-weighted spin-echo (SE), T1-weighted turbo SE (TSE), proton-density (PD)- and T2-weighted TSE, T1-weighted gradient echo (GE), short-tau inversion recovery (STIR), multi echo data image combination (MEDIC), T2*-weighted GE, volumetric interpolated breath-hold examination (VIBE), and dual-echo steady state (DESS). For each sequence, we evaluated the visibility of the anatomical structures of the central pivot, lateral compartment, and anterior compartment using a semiquantitative score (0=total masking; 1=insufficient visibility; 2=sufficient visibility; 3=optimal visibility). The sum of the scores given to each sequence was divided by the maximal sum, obtaining a percentage visibility index. Friedman and sign tests were used for statistical analysis. MR examination time was 30-32 min. No patients reported pain, heat or other local discomfort. The visibility index ranged between 83% and 89% for the first four sequences without significant differences among them, 58% for STIR and 11%-36% for the last five sequences. Significant differences were found between each of the four first sequences and the remaining sequences (p<0.004) and between STIR and the last five sequences (p<0.008). MR imaging of the knee after medial unicompartmental arthroplasty was not associated with adverse events. An imaging protocol including SE, TSE and STIR sequences could be used to study the knee with unicompartmental arthroplasty.

A multispectral three‐dimensional acquisition technique for imaging near metal implants

Metallic implants used in bone and joint arthroplasty induce severe spatial perturbations to the B0 magnetic field used for high-field clinical magnetic resonance. These perturbations distort slice-selection and frequency encoding processes applied in conventional two-dimensional MRI techniques and hinder the diagnosis of complications from arthroplasty. Here, a method is presented whereby multiple three-dimensional fast-spin-echo images are collected using discrete offsets in RF transmission and reception frequency. It is demonstrated that this multi acquisition variable-resonance image combination technique can be used to generate a composite image that is devoid of slice-plane distortion and possesses greatly reduced distortions in the readout direction, even in the immediate vicinity of metallic implants.

Improved Thermographic Object Detection relying-on Image Processing and Sensorial Development of Quantum IR Photodetectors

The manuscript is devoted to improve of the thermographic object detection. This research work complies two phases. The first phase of this framework focuses on the enhancement of quantum IR photodetectors (QIRPs) performance based on studying the responsivity (R) and its dependence on QIRPs parameters. The semiconductor-based QRIPs include quantum (well QWIPs, wire QRIPs, and dot QDIPs) photodetectors. Moreover, the study is directed to the most sensitive device among these three quantum types. While the second phase of the study presents robust object detection in IR images by employing multistage efficient image processing techniques. Firstly, we enhance the quality of the images to simplify the process of object extraction. This enhancement, preprocessing, relies mainly on denoising and histogram quantization. Secondly, the segmentation phase is employed to extract the object under interest.The segmentation is implemented based on fuzzy c-mean clustering.

Multihit two-dimensional charged-particle imaging system with real-time image processing at 1000 frames/s

A high-speed imaging system developed for two-dimensional counting of charged particles is presented. Microchannel plates coupled with a phosphor screen of a short emission lifetime (<1 micros) are used to visualize the two-dimensional positions of charged-particle impacts, and the image on the phosphor screen is captured with a 1 kHz complementary metal oxide semiconductor (CMOS) image sensor (512x512 pixels). A multistage image intensifier consisting of the first and second generation devices was used to compensate for the low sensitivity of CMOS. The centers of gravity (COG) of individual light spots in each image frame are calculated in real time by a field programmable gate array circuit. The performance of this system is tested by time-resolved photoelectron imaging (TR-PEI) of NO using (1+1(')) resonance enhanced multiphoton ionization via the A (2)Sigma(+) state with a femtosecond laser operated at 1 kHz. The new system enabled COG detection for more than ten particles in each frame at 1 kHz and achieved an extremely high degree of accuracy in the measurement of photoelectron angular distributions in TR-PEI.

DWI of the spinal cord with reduced FOV single‐shot EPI

Single-shot echo-planar imaging (ss-EPI) has not been used widely for diffusion-weighted imaging (DWI) of the spinal cord, because of the magnetic field inhomogeneities around the spine, the small cross-sectional size of the spinal cord, and the increased motion in that area due to breathing, swallowing, and cerebrospinal fluid (CSF) pulsation. These result in artifacts with the usually long readout duration of the ss-EPI method. Reduced field-of-view (FOV) methods decrease the required readout duration for ss-EPI, thereby enabling its practical application to imaging of the spine. In this work, a reduced FOV single-shot diffusion-weighted echo-planar imaging (ss-DWEPI) method is proposed, in which a 2D spatially selective echo-planar RF excitation pulse and a 180 degrees refocusing pulse reduce the FOV in the phase-encode (PE) direction, while suppressing the signal from fat simultaneously. With this method, multi slice images with higher in-plane resolutions (0.94 x 0.94 mm(2) for sagittal and 0.62 x 0.62 mm(2) for axial images) are achieved at 1.5 T, without the need for a longer readout.

Application of MultiScale Hidden Markov Modeling Wavelet Coefficients to fMRI Activation Detection

Problem Statement: The problem of detection of functional magnetic resonance images (fMRIs), that is, to decide active and nonactive regions of human brain from fMRIs is studied in this paper. fMRI research is finding and will find more and more applications in diagnosing and treating brain diseases like depression and schizophrenia. At its initial stage fMRI detection are pixel-wise methods, which do not take advantage of mutual information among neighboring pixels. Ignoring such spatial information can reduce detection accuracy. During past decade, many efforts have been focusing on taking advantage of spatial correlation inherent in fMRI data. Most well known is smoothing using a fixed Gaussian filter and the compensation for multiple testing using Gaussian random field theory as used by Statistical Parametric Mapping (SPM). Other methods including wavelets had also been proposed by the community. Approach: In this study a novel two-step approach was put forward that incorporates spatial correlation information and is amenable to analysis and optimization. First, a new multi scale image segmentation algorithm was proposed to decompose the correlation image into several different regions, each of which is of homogeneous statistical behavior. Second, each region will be classified independently as active or inactive using existing pixel-wise test methods. The image segmentation consists of two procedures: Edge detection followed by label estimation. To deduce the presence or absence of an edge from continuous data, two fundamental assumption of our algorithm are 1) each wavelet coefficient was described by a 2-state Gaussian Mixture Model (GMM); 2) across scale, each state is caused by its parent state, hence the Multiscale Hidden Markov Model (MHMM). The states of Markov chain are unknown (hidden) and represent the presence (state 1) or absence (state 0) of edges. Using this interpretation, the edge detection problem boils down to the posterior state estimation given observation. Results: Data processing results demonstrate much improved efficiency of our algorithm compared with pixel-wise detection methods. Conclusions: Our methods and results stress the importance of spatial-temporal modeling in fMRI research.

ＡＬＯＳ／ＰＡＬＳＡＲデータを用いた植生地域における岩相識別

Lithofacies discrimination has been conducted using the full- and dual-polarimetric ALOS/PALSAR data in the semi-vegetated Kalgoorlie area, WA, Australia and vegetated Lao Cai area, Northern Vietnam. In the Kalgoorlie area, characteristic features of topography, vegetation and surface roughness affected by the difference of geological units were noticed from simple scatter component image of PALSAR and multi frequency image using PALSAR and ENVISAT/ASAR data. In the Lao Cai area, the relation between polarimetric characters data and lithofacies are not recognized due to the thick vegetation and steep slope, however, the geological boundaries between metamorphic, igneous and sedimentary rocks are detected by using coherence information which is computed within InSAR processing.

Automatic detection of ground glass opacity by use of the density features on multi detector-row CT images

Automatic detection of ground glass opacity by use of the density features on multi detector-row CT images

A Survey and Analysis of Pixel Level Multisensor Medical Image Fusion using Discrete Wavelet Transform

Medical image fusion has become an active research topic due to advances in sensor technology, microelectronics, processing techniques that combine information from different sensors into a single composite image for analysis, interrelation and better clinical diagnosis in much rapid and accurate way. The main objective of medical image fusion using wavelets is to create new image by regrouping the complementary information of multi sensor output.Various medical imaging modalities such as X-ray, Ultrasonics, Computed tomography (CT) magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission tomography (SPECT). This work presents the survey of existing fusion schemes and a novel approach of medical image fusion using Discrete wavelet transform (DWT). The challenge is thus to fuse multi sensor medical images with spectral aspects of low resolution and spatial aspects of a high-resolution image, with out introducing artifacts. In this research multilayer data sets are also tested apart from CT & MRI images. The DWT is applied independently to obtain a set of approximation coefficients, horizontal coefficients, vertical coefficients, and diagonal coefficients for two images. The different independent bands are fused to get the combined output. By applying the Inverse Discrete wavelet transform (IDWT)the fused image is reconstructed. The simulation of fused image when consulted with radiologist and physician, proved better clarity and have high information compared to the first two sensor images. Hence the clinical diagnosis is much faster and accurate.

Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events

Saliency is an important perceptual cue that occurs at different levels of resolution. Important attributes of saliency are symmetry, continuity, and closure. Detection of these attributes is often hindered by noise, variation in scale, and incomplete information. This paper introduces the iterative voting method, which uses oriented kernels for inferring saliency as it relates to symmetry. A unique aspect of the technique is the kernel topography, which is refined and reoriented iteratively. The technique can cluster and group nonconvex perceptual circular symmetries along the radial line of an object's shape. It has an excellent noise immunity and is shown to be tolerant to perturbation in scale. The application of this technique to images obtained through various modes of microscopy is demonstrated. Furthermore, as a case example, the method has been applied to quantify kinetics of nuclear foci formation that are formed by phosphorylation of histone gammaH2AX following ionizing radiation. Iterative voting has been implemented in both 2-D and 3-D for multi image analysis.

The 3DX multi image micro-CT device in clinical dental practice

To evaluate the usefulness of 3DX multi-image micro-CT device in clinical dental practice. Images of 198 examinations performed with a 3DX device during a 6 month period in a private dental practice were evaluated retrospectively for the benefit of additional information compared with panoramic or intraoral radiographs. The main indication for the use of 3DX micro-CT was planning of dental implant placement in 49% of the examinations. Diagnosis or exclusion of dental infection or peri-implantitis represented 28% of the examinations and tooth, root or foreign body localization represented 13%. Temporomandibular joint (TMJ) imaging and cyst or tumour diagnosis represented 7.5% and 2.5% of the examinations, respectively. In implant planning and tooth, root or foreign body localization examinations, the required information was obtained in every case except three implant planning examinations, in which the exact measurements were hampered by artefacts caused by root fillings and retrograde fillings or metal posts. When compared with conventional radiography, additional radiographic information was obtained in 51% of the micro-CT examinations performed to confirm or exclude dental infection or peri-implantitis. Micro-CT also showed the bone structure of TMJs precisely. The 3DX micro-CT device visualizes bony anatomical structures precisely, which makes it a reliable tool for, for example, planning of implant treatment. The device is highly suitable for diagnosing dental infections. Dental restorations may cause disturbing artefacts and in 4.5% of the examinations the small imaging area resulted in re-examination.

Multi secret image color visual cryptography schemes for general access structures *

Abstract In the proposed visual cryptography schemes for general access structures, the single secret image black-and-white visual cryptography schemes with meaningful shares have been constructed, in which the shares are innocent looking images. The meaningful shares have not been realized in single secret image color schemes; neither have the multi secret images color schemes. In this paper, the multi secret images color visual cryptography schemes for general access structures with meaningful shares are constructed by the method of matrix concatenation, the pixel expansion is obtained, and the validity of the scheme is proven. In our scheme, the different combination of meaningful color shares can be used to recover distinct color secret images. The multi secret images black-and-white visual cryptography scheme is a special case of our color scheme. *Supported by National Natural Science Foundation of China (Grant No. 90304014) and the Major State Basic Research Development Program of China (Grant No. ...

A case of inferior nerve paralysis successfully diagnosed by Multi Image Micro CT

A case of inferior nerve paralysis successfully diagnosed by Multi Image Micro CT

Computer-aided segmentation and quantification of the human airway tree on the basis of multi CT images

We developed an algorithm for automated segmentation and quantification of the human airway tree from the CT images of the lung. The algorithm consists of segmentation of the airway tree, its skeletonization, identification of the airway generation, and measurement of volume, length and diameter of each airway. The airway lumen was extracted from CT images with the region growing method. For morphometric identification of airways' branching, the 3-D airway model was skeletonized. The results showed it was possible to extract up to the 14th generation of the airway tree whose diameter was 1.7mm. The airway diameters obtained were in good agreement with those reported in autopsy study, although the diameters on and after 8th generation were all larger than those in vivo measurement due probably to an insufficient resolution of CT images. These results addressed the potential of the algorithm developed in this study for quantification of the airway tree.

Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure

A new multistage method using hierarchical clustering for unsupervised image classification is presented. In the first phase, the multistage method performs segmentation using a hierarchical clustering procedure which confines merging to spatially adjacent clusters and generates an image partition such that no union of any neighboring segments has homogeneous intensity values. In the second phase, the segments resulting from the first stage are classified into a small number of distinct states by a sequential merging operation. The region-merging procedure in the first phase makes use of spatial contextual information by characterizing the geophysical connectedness of a digital image structure with a Markov random field, while the second phase employs a context-free similarity measure in the clustering process. The segmentation procedure of region merging is implemented as a hierarchical clustering algorithm whereby a multiwindow approach using a pyramid-like structure is employed to increase computational efficiency while maintaining spatial connectivity in merging. From experiments with both simulated and remotely sensed data, the proposed method was determined to be quite effective for unsupervised analysis. In particular, the region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns.

A hierarchical multi-stage data fusion approach to non-destructive evaluation of aero engine components

This paper describes a multi-stage image analysis system used to monitor the condition of aero engine components. A hierarchical framework, of analysis requirements was constructed then algorithms were developed to fulfilthe requirements. The components are inspected using the fluorescent dye penetrant inspection (FPI) technique. This allows surface-breaking discontinuities to be detected, helping to ensure quality standards are being met. The multi-stage algorithm developments automate the FPI inspection using a data fusion approach. Raw image data, inspection knowledge and extracted image data are all fused to reach an inspection decision. The paper describes the main developments within the hierarchical process from image acquisition through to image processing, region selection, feature extraction, knowledge encoding and the final component inspection classification. Finally, the commercial implementation and performance issues associated with the system are discussed.

An automated seamless mosaicing system of multi-charge coupled devices of panchromatic data

Panchromatic data of pixel resolution 5.8 m obtained from IRS-1C and IRS-1D satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.

Tumor hypoxia and blood vessel detection: an image analysis technique for simultaneous tumor hypoxia grading and blood vessel detection in tissue sections.

We have developed a multistage image analysis technique for the simultaneous segmentation of blood vessels and hypoxic regions in dual-stained tumor tissue sections. The algorithm, which is integrated in a task-oriented image analysis system developed on-site, initially uses the K-nearest neighbor classification rule in order to label the image pixels. Classification is based on a training set selected from manually drawn regions corresponding to the areas of interest. If the output image contains a significant number of misclassified pixels, the user has the option to apply a series of specific problem-designed routines (texture analysis, fuzzy c-means clustering, and edge detection) in order to improve the final segmentation result. Validation experiments indicate that the algorithm can robustly detect these biological features, even in tissue sections with a very low quality of staining. This approach has also been combined with other image analysis based procedures in order to objectively obtain quantitative measurements of potential clinical interest.

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.