Multi Images Research Articles

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.

Necklaces: Inhomogeneous and Point-Enhanced Deformable Models

In many advanced segmentation problems objects have inhomogeneous boundaries, hindering segmentation under uniform boundary assumption. We present a multifeature image segmentation method, called necklaces, that exploits local inhomogeneities to reduce the complexity of the segmentation problem. Multiple continuous boundary features, deduced from a set of training objects, are statistically analyzed and encoded into a deformable model. On the deformable model salient features are identified on the basis of the local differential geometric characteristics of the features, yielding a classification into point landmarks, curve landmarks, and sheet points. Salient features are exploited within a priority segmentation scheme that tries to find complete boundaries in an unknown image, first by landmarks and then by sheet points. The application of our method to segment vertebrae from CT data shows promising results despite their articulated morphology and despite the presence of interfering structures.

2144 3DX Multi Image Micro CT 用 3 次元画像処理法に関する研究

2144 3DX Multi Image Micro CT 用 3 次元画像処理法に関する研究

微小流路内における三次元計測法

A method of three dimensional velocities measurement is applied to flow in micro channel. Method of using stereo camera is ordinarily used as three dimensional PIV method. However the stereo camera method has complicated processes, such as setting camera parameters, matching spatial and time correlation between two corresponding stereo images, calculating the three dimensional velocities. The dimensions of channel for the experiment are 0.17 millimeters width and 0.61 millimeters depth. Water under the room temperature flows vertically in the channel. It is supposed the velocity component perpendicular to main flow is small. In this case, parallel multi images perpendicular to ray of camera as parts of three dimensional flow are useful. Parallel images can obtain by making to change distance from camera to subject. The method in this paper is setting a thin glass between camera and subject. This method is simpler than stereo camera method.

A study on the three-dimensional image processing method for 3DX Multi Image Micro CT

Objective: A clinical model of limited cone-beam X-ray CT (3DX Multi Image Micro CT) has been developed by Arai et al. [Y. Arai, E. Tammisalo, K. Iwai, K. Hashimoto, K. Shinoda, Development of ortho cubic super high resolution CT (Ortho-CT), Car '98 Computer-Assisted Radiology and Surgery, Elsevier, Amsterdam, 1998, pp. 780–785] for dental use. In this paper, a three-dimensional image processing method for 3DX Multi Image Micro CT (3DX), which is capable of obtaining clear three-dimensional images, is proposed. Method: First of all, the three-dimensional projection data are decomposed to a set of two-dimensional data. However, the obtained data contain much noise because the projection data are obtained by low-level X-ray. Therefore, we proposed the following steps for obtaining clear three-dimensional images. Firstly, contrast stretching is applied to the set of two-dimensional image data based on the contrast information of the target image for avoiding noise. Then, the edge detection with the Canny operator is performed. In our method, the loss of the target image, which is caused by noise reduction and edge detection, can be interpolated by the image obtained from different directions. Finally, the clear edge of the two-dimensional image data is reconstructed to the three-dimensional image data. Result: It is shown that clear three-dimensional images are obtained when we applied the proposed method for three-dimensional imaging of the temporomandibular joint. Conclusion: A new method for constructing three-dimensional images from the image data obtained from limited cone-beam-type X-ray CT is proposed. The effectiveness of the proposed method is shown by constructing three-dimensional images of the temporomandibular joint.

Practical model “3DX” of limited cone-beam X-ray CT for dental use

Objective: The authors have developed a clinical model of limited cone-beam X-ray CT for dental use and started to use the model in clinical practice. It is called “3DX multi image micro CT” (3DX, J. Morita, Kyoto, Japan). Presented here is a report about the result. Method: We made a design of limited cone-beam X-ray CT so that it could be most effective for dentistry use. This machine is a kind of cone-beam type X-ray CT equipped with a 4-in. imaging intensifier (II) as a sensor. The size of the X-ray at its rotational center is 29 mm high and 39 mm wide. While a subject is seated for shooting, a focal spot and the X-ray rotational center are put into agreement with a light beam of X, Y and Z directions. Images are taken under the exposure condition of 80 kV (X-ray tube voltage) and 2 mA (X-ray tube electric current). Exposure time is 17 s, during which 512 frames of two-dimensional images are recorded. These images are reconstructed with a personal computer into digital images in a columnar field of 38 mm in diameter and 29 mm in height. It takes about 10 min to carry out arithmetic calculation for the image reconstruction. Clinical use of this machine started in January 2001 at the Department of Radiology, Nihon University of Dental Hospital. Result: The 3DX produced highly clear, three-dimensional tomographic images of impacted teeth. Conclusion: Since the 3DX is capable of producing clear images with a small radiation dose, we consider it to be effective as a tool to support three-dimensional imaging diagnosis for dental implants, impacted teeth, etc.