Three-dimensional optical microscopy using axially distributed image sensing

We propose three-dimensional (3D) optical microscopy using axially distributed image sensing. In the proposed method, the micro-objects are optically magnified and their axially distributed images are recorded by moving the image sensor along a common optical axis. The 3D volumetric images are generated from the recorded axial image set using a computational reconstruction algorithm based on ray backprojection. Preliminary experimental results are presented. To the best of our knowledge, this is the first report on 3D optical microscopy using axially distributed sensing.     

Three-dimensional free view reconstruction in axially distributed image sensing

In this Letter, we propose a three-dimensional(3D) free view reconstruction technique in axially distributed image sensing(ADS). In typical integral imaging, free view reconstructed images can be obtained by tilting all elemental images or tilting the reconstruction plane due to large lateral perspectives for 3D objects. In conventional ADS, the reconstructed images at only a front view can be generated since the sensor is moved along with its optical axis so that it has small lateral perspectives for 3D objects. However, the reconstructed 3D images at any viewing point may be obtained because the virtual viewing camera may capture these slightly different perspectives for 3D objects. Therefore, in this Letter, we employ the virtual viewing camera to visualize the 3D images at the arbitrary viewing point. To support our proposed method, we show the experimental results.     

Three-dimensional recognition of occluded objects by using computational integral imaging

We have proposed a method to recognize partially occluded three-dimensional (3D) objects by using 3D volumetric reconstruction integral imaging (II). An II system captures multiple perspectives of occluded objects by using a microlens array. The reconstruction of the occluded 3D scene and target recognition are done digitally to reduce the effects of the occlusion. To verify system performance, we have implemented an optimum filter for object recognition. Both two-dimensional (2D) images and 3D II volumetric reconstructed images are considered. The correlation results of occluded 3D images for volumetric reconstruction show substantial improvements compared with those for conventional 2D imaging of occluded images.     

Three-dimensional visualization of objects in scattering medium using integral imaging and spectral analysis

This paper presents a three-dimensional visualization method of 3D objects in a scattering medium. The proposed method employs integral imaging and spectral analysis to improve the visual quality of 3D images. The images observed from 3D objects in the scattering medium such as turbid water suffer from image degradation due to scattering. The main reason is that the observed image signal is very weak compared with the scattering signal. Common image enhancement techniques including histogram equalization and contrast enhancement works improperly to overcome the problem. Thus, integral imaging that enables to integrate the weak signals from multiple images was discussed to improve image quality. In this paper, we apply spectral analysis to an integral imaging system such as the computational integral imaging reconstruction. Also, we introduce a signal model with a visibility parameter to analyze the scattering signal. The proposed method based on spectral analysis efficiently estimates the original signal and it is applied to elemental images. The visibility-enhanced elemental images are then used to reconstruct 3D images using a computational integral imaging reconstruction algorithm. To evaluate the proposed method, we perform the optical experiments for 3D objects in turbid water. The experimental results indicate that the proposed method outperforms the existing methods.     

Three-dimensional integral imaging with improved visualization using subpixel optical ray sensing

In this Letter, we propose an improved three-dimensional (3D) image reconstruction method for integral imaging. We use subpixel sensing of the optical rays of the 3D scene projected onto the image sensor. When reconstructing the 3D image, we use a calculated minimum subpixel distance for each sensor pixel instead of the average pixel value of integrated pixels from elemental images. The minimum subpixel distance is defined by measuring the distance between the center of the sensor pixel and the physical position of the imaging lens point spread function onto the sensor, which is projected from each reconstruction point for all elemental images. To show the usefulness of the proposed method, preliminary 3D imaging experiments are presented. Experimental results reveal that the proposed method may improve 3D imaging visualization because of the superior sensing and reconstruction of optical ray direction and intensity information for 3D objects.     

3D reconstruction of objects using stereo imaging

This paper describes two stereo imaging experimental set-ups useful in measuring the 3D geometry of objects: a parallel optical-axis model and a converging optical-axis model. Digital image correlation is used to find the disparities between corresponding points in a pair of images, for each of these models, with subpixel accuracy. To show the application of the developed algorithms and the stereo imaging experimental set-ups, four different body geometries are used. For some of the objects tested a higher measuring accuracy is obtainable from the converging optical-axis experimental set-up.     

Enhanced computational integral imaging system for partially occluded 3D objects using occlusion removal technique and recursive PCA reconstruction

In this paper, we propose an enhanced computational integral imaging system by both eliminating the occlusion in the elemental images recorded from the partially occluded 3D object and recovering the entire elemental images of the 3D object. In the proposed system, we first obtain the elemental images for partially occluded object using computational integral imaging system and it is transformed to sub-images. Then we eliminate the occlusion within the sub-images by use of an occlusion removal technique. To compensate the removed part from occlusion-removed sub-images, we use a recursive application of PCA reconstruction and error compensation. Finally, we generate the entire elemental images without a loss from the newly reconstructed sub-images and perform the process of object recognition. To show the usefulness of the proposed system, we carry out the computational experiments for face recognition and its results are presented. Our experimental results show that the proposed system might improve the recognition performance dramatically.     

Recovery of partially occluded objects by applying compressive Fresnel holography

A compressive Fresnel holography approach is suggested for the recovery of partially occluded objects. Reconstruction guarantees are analyzed and the effectiveness of the method is demonstrated using simulations and an experimental result showing the reconstruction of a partially occluded resolution chart.     

Occlusion removal technique for improved recognition of partially occluded 3D objects in computational integral imaging

In this paper, we propose an occlusion removal technique for improved recognition of 3D objects that are partially occluded in computational integral imaging (CII). In the reconstruction process of a 3D object which is partially occluded by other objects, occlusion degrades the resolution of reconstructed 3D images and thus this affects negatively the recognition of a 3D object in CII. To overcome this problem, we introduce a method to eliminate occluding objects in elemental image array (EIA) and the proposed method is applied to 3D object recognition by use of CII. To our best knowledge, this is the first time to remove occlusion in CII. In our method, we apply the elemental image to sub-image (ES) transform to EIA obtained by a pickup process and those sub-images are employed for occlusion removal. After the transformation, we correlate those sub-images with a reference sub-image to locate occluding objects and then we eliminate the objects. The inverse ES transform provides a modified EIA. Actually, the modified EIA is considered to be an EIA without the object that occludes the object to be reconstructed. This can provide a substantial gain in terms of the image quality of 3D objects and in terms of recognition performance. To verify the usefulness of the proposed technique, some experimental results are carried out and the results are presented.     

Regeneration of elemental images in integral imaging for occluded objects using a plenoptic camera

In this Letter, we propose an elemental image regeneration method of three-dimensional(3D) integral imaging for occluded objects using a plenoptic camera. In conventional occlusion removal techniques, the information of the occlusion layers may be lost. Thus, elemental images have cracked parts, so the visual quality of the reconstructed 3D image is degraded. However, these cracked parts can be interpolated from adjacent elemental images. Therefore, in this Letter, we try to improve the visual quality of reconstructed 3D images by interpolating and regenerating virtual elemental images with adjacent elemental images after removing the occlusion layers. To prove our proposed method, we carry out optical experiments and calculate performance metrics such as the mean square error(MSE) and the peak signal-to-noise ratio(PSNR).     

Reconstruction in image space using basis functions for partially parallel imaging

General theory of a new reconstruction technique for partially parallel imaging (PPI) is presented in this study. Reconstruction in Image space using Basis functions (RIB) is based on the general principle that the PPI reconstruction in image space can be represented by a pixel-wise weighted summation of the aliased images directly from undersampled data. By assuming that these weighting coefficients for unaliasing can be approximated from the linear combination of a few predefined basis functions, RIB is capable of reconstructing the image within an arbitrary region. This paper discusses the general theory of RIB and its relationship to the classical reconstruction method, GRAPPA. The presented experiments demonstrate RIB with several MRI applications. It is shown that the performance of RIB is comparable to that of GRAPPA. In some cases, RIB shows advantages of increasing reconstruction efficiency, suppressing artifacts and alleviating the nonuniformity of noise distribution. It is anticipated that RIB would be especially useful for cardiac and prostate imaging, where the field of view during data acquisition is required to be much larger than the region of interest.     

Anaglyph stereo visualization by the use of a single image and depth information

In this paper, an anaglyph stereo visualization is studied by the use of a single image and depth information. The present technique allows the stereo visualization of the target image without binocular camera, which has been used for generating the standard anaglyphs. Three test cases are shown in this work, which covers the generation of monochrome anaglyph from given geometrical information, and that of monochrome and color anaglyph from depth information evaluated from other imaging devises, which are placed in parallel. The experimental depth information of the target image is evaluated from the correlation-based template matching analysis of the random dot patterns optically projected on the target objects. Examples of anaglyph stereo visualization are shown for scientific art, such as the monochrome cube, monochrome plaster figure and the color artificial flowers. These results indicate that the present technique of anaglyph stereo visualization through the depth information is very useful and widely applicable to the general three-dimensional visualization.     

Three-dimensional visualization of diffusion flame shapes under acoustic excitation using stereoscopic imaging and reconstruction technique

Stereoscopic imaging and reconstruction technique is introduced to reconstruct the flame structures that are subject to acoustic excitation. The laminar diffusion flame under investigation was created in a cylindrical tube and excited by a loudspeaker. Stereo images were taken at a shutter speed of 1/10000^th second using a ‘stereo camera’ and the depth cue of the flame structures along the camera viewing direction were then computed using machine vision methodology. By visualizing the computed three-dimensional flame models, as well as judging the corresponding attribute such as surface gradient, the understanding of the flame and acoustic wave interaction could be improved.     

Three-dimensional image authentication using binarized images in double random phase integral imaging

We proposed a three-dimensional(3D) image authentication method using binarized phase images in double random phase integral imaging(InI). Two-dimensional(2D) element images obtained from InI are encoded using a double random phase encryption(DRPE) algorithm. Only part of the phase information is used in the proposed method rather than using all of the amplitude and phase information, which can make the final data sparse and beneficial to data compression, storage, and transmission. Experimental results verified the method and successfully proved the developed 3D authentication process using a nonlinear cross correlation method.     

Three-dimensional image correlator using fast computational integral imaging reconstruction method based on pixel-to-pixel mapping

In this paper, a three-dimensional (3D) image correlator using a fast computational integral imaging reconstruction (CIIR) method based on a pixel-to-pixel mapping is proposed. In order to implement the fast CIIR method, we replace the magnification process in the conventional CIIR by a pixel-to-pixel mapping. The proposed fast CIIR method reconstructs two sorts of plane images; a plane image whose quality is sufficient, and a dot pattern plane image insufficient to view. This property is very useful to enhance the performance of a CIIR-based image correlator. Thus, we apply the fast CIIR method to a CIIR-based image correlator. To show the feasibility of the proposed method, some preliminary experiments on both pattern correlation and computational cost are carried out, and the results are presented. Our experimental results indicate that the proposed image correlator is superior to the previous method in terms of both correlation performance and complexity.     

Three-dimensional orientation of microscopic objects using combined elliptical and point optical tweezers

We report the development of a simple approach for controlled three-dimensional orientation of microscopic objects. The approach makes use of the fact that whereas in an elliptical trap, an object lacking spherical symmetry orients with its long axis along the major axis of the elliptical tweezers, in point tweezers it orients with its long axis along the trapping beam. Therefore, a combined use of point and elliptical tweezers and a control over the relative power of the two trapping beams can be used for three-dimensional orientation of the object. The use of this approach for three-dimensional orientation of biological objects varying in size from 2 to 20 μm is discussed. PACS 07.60.-j; 87.80.Cc     

A simple application of compressed sensing to further accelerate partially parallel imaging

Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.     

Three-dimensional susceptibility-weighted imaging at 3 T using various image analysis methods in the estimation of grading intracranial gliomas

Object

Although three-dimensional (3D), high-spatial resolution susceptibility-weighted imaging (SWI) appears to be valuable in the evaluation of central nervous system gliomas, several evaluation methods are proposed in the literature. The purpose of this study was to evaluate the use of 3D SWI for grading intracranial gliomas with various analysis methods.

Materials and Methods

Twenty-three patients suspected of having gliomas participated in this study. SWI was performed in addition to conventional MR sequences. In 15 cases, post-gadolinium enhanced SWI was also obtained. Imaging evaluation criteria were conventional grade, hypointensity ratio in the tumor-dominant structure of hypointensity on SWI (hemorrhage or vascular structure) and presence of abnormal enhancement surrounding the tumor.

Results

Mean grading scores of conventional grade showed no statistically significant difference among WHO grades. Mean grading scores of hypointensity ratios in the tumor were higher for WHO Grades 3 and 4 than for lower grade tumors (P=.05, Mann–Whitney U test). Hemorrhagic foci were more frequently seen in the higher grade tumor. Post-contrast susceptibility-weighted images of five of 11 WHO Grade 3 and 4 cases showed bright enhancement surrounding the tumor, suggesting a breakdown of the blood–brain barrier.

Conclusions

SWI at 3 T may be a useful method to analyze the structural characteristics of gliomas and to evaluate pathology in vivo. Assessment of hypointensity ratios in the glioma was the most preferable method in grading glioma. However, more studies, specifically concerning a suitable method for image analysis, are needed to establish SWI at 3 T as a useful tool in clinical routine.