基于I-BGLAM纹理和光谱融合的高光谱显微成像木材树种分类

为了提高木材树种分类的正确率,提出了一种基于I-BGLAM纹理特征和光谱特征融合的高光谱图像的木材树种分类方法。实验数据是利用SOC710VP高光谱成像仪获取的可见光/近红外（372.53~1 038.57 nm）范围内的高光谱图像。首先,利用基于OIF的特征波段选择方法降低高光谱图像的维数,选择出含有信息量大的波段。其次,对选择出的波段图像使用NSCT及NSCT逆变换得到融合图像,对得到的融合图像使用I-BGLAM提取其纹理特征。与此同时,对高光谱图像的全波段求取平均光谱并进行S-G（Savitzky-Golay）平滑得到光谱特征。最后,将得到的纹理特征和光谱特征融合后送进极限学习机（ELM）中进行分类。此外,还和基于灰度共生矩阵（GLCM）的木材识别的传统方法以及近几年木材树种识别领域内被提出的主流方法进行了比较。该研究主要创新点有两个：一是将强纹理提取器I-BGLAM用于高光谱图像中提取其纹理特征;二是提出一种新的特征融合的模型用于高光谱图像的分类。针对8个树种的实验结果表明,单独使用I-BGLAM提取的纹理特征来进行分类的正确率最高可到达88.54%,而使用GLCM提取纹理特征的传统方法正确率最高只有76.04%,该结果可以得出本文使用I-BGLAM在纹理特征提取方面要优于GLCM,这为后面建立的融合模型打下很好的基础,单独使用平均光谱特征来分类的正确率最高可以达到92.71%,使用所提出的特征融合方法所得到的分类正确率最高可达到100%,这说明使用所提出的融合模型来分类要比以前单独使用某一种特征的分类模型要好。此外,使用所提出的方法得到的分类正确率要高于本领域内其他两种主流的识别方法。因此,所提出的基于I-BGLAM纹理特征和光谱特征融合的方法能够提高木材树种分类的正确率,该方法在木材树种分类方面有着一定的利用价值。     

A method of boundary estimation from 3D scattered point data without normals by implicit function and Delaunay tetrahedralization

Abstract  

A method of boundary estimation from 3D scattered point data has been proposed. For estimating a boundary, the implicit function-based method and the Delaunay tetrahedralization are mainly used in the proposed method. An advantage of the proposed method is that point coordinates are only required as input data. Namely, normals on each of given points are not required as input data. Instead, the point normals are estimated three times. After each procedure for estimating point normals, the accuracy of the estimated normals may be better. Therefore, the geometric structure of a surface generated with the estimated normals is gradually closer to the original surface. Numerical experiments demonstrate that the proposed method enables to estimate an expected boundary without normals as input data. In addition, the performance of the proposed method is numerically investigated. The estimated boundary can be obtained as an implicit surface or as a set of triangles.     

A robust security framework for 3D images

Abstract  

Three-dimensional (3D) visualization of spatial and non-spatial data is a well-established practice having numerous applications. The cheapest and the most efficient way to 3D visualization is 3D images/Anaglyphs. 3D images contain 3D information of the objects present in the image. These images are easily obtained by superimposing left and right eye images in different color in a single image. In this paper, a novel security framework, viz., watermarking scheme, is presented to ensure their security. The proposed security framework is employed in fractional Fourier transform domain of secret color channel followed by the embedding using singular value decomposition. The secret channels (SEC) are obtained by applying reversible integer transform on the RGB channels. The experimental results prove the robustness and imperceptibility of the proposed watermarking scheme.     

CUDA-accelerated simulation of multiple scattering using decoupling approximation

Abstract  

In this research a CUDA-accelerated simulation method of multiple scattering in dense participating media based on decoupling approximation is proposed. Simulation of multiple scattering is important, in that it visually illustrates how radiance transfers between gaseous particles. The existing radiance transfer equation has poor computational performance because of its complicated integral calculation of in-scattering in each radiance update. However, this computation can be avoided using previous cached results proposed in this paper. To construct the cached results, we first decouple the phase function in radiance transfer equation with singular value decomposition (SVD) approximation. SVD approximates the multivariate phase function as a sum of products of incident light and outgoing light of lower dimensionality. Then the incident light and outgoing light data of current radiance update are cached independently in two textures. The cached two textures are used to replace integral calculation of in-scattering in the next radiance update to improve computation performance. The proposed method is designed in a parallelized way so that the parallel computing power of CUDA can be fully exploited. The simulation results show that our method allows fast rendering of dynamic scenes while high accuracy is maintained.     

A new experimental technique for generating anaglyph stereo images for sculpture arts

Abstract  

In this paper, a new experimental technique for generating anaglyph stereo images for three-dimensional visualization of sculpture arts is proposed. The images are taken at fixed distance using the imaging system consisting of single CCD camera and turntable. This system allows generation of stereo images at variable distances from the target object. The feasibility study of this technique is demonstrated by showing some anaglyph stereo images of a sculpture art.     

Statistical evaluation of methods for quantifying gene expression by autoradiography in histological sections

Background  

In situ hybridisation (ISH) combined with autoradiography is a standard method of measuring the amount of gene expression in histological sections, but the methods used to quantify gene expression in the resulting digital images vary greatly between studies and can potentially give conflicting results.     

Removal of partial volume averaging with free water in MR diffusion tensor imaging using inversion recovery for b0 image

Purpose

To remove the partial volume averaging effect of free water in MR diffusion imaging of neural tissues by use of the fluid attenuated inversion recovery (FLAIR) without the penalty of an extended scan time.

Materials and methods

The magnetic resonance images were obtained from a normal volunteer in a coronal slice orientation at 3 T with the 20-channel rf coil. In diffusion imaging only the b0 images were obtained with the FLAIR contrast while the diffusion weighted images were obtained without the FLAIR contrast. A composition of FLAIR b0 and non-FLAIR diffusion weighted images was used in calculating the diffusion tensor and fractional anisotropy after compensating the reduced signal amplitude due to the inversion recovery in the FLAIR b0 images. The fractional anisotropy of the non-FLAIR, FLAIR, and the composite methods were analyzed for the mean and histogram in the corpus callosum, cervical spine, and the fornix tracts.

Results

The partial volume averaging effect was observed in the corpus callosum, the cervical spine, and the fornix tracts in the non-FLAIR b0 and diffusion images. The partial volume averaging effect was removed in the FLAIR diffusion images which took more than twice the scan time than the non-FLAIR diffusion imaging. The proposed composite FLAIR diffusion imaging removed the partial volume averaging effect as in the FLAIR diffusion imaging. The distribution of the FA histogram was very different between the non-FLAIR and FLAIR diffusion images, while it was very similar between the FLAIR and the composite FLAIR after correcting the white matter signal in the FLAIR b0 images.

Conclusions

The proposed composite FLAIR diffusion imaging method was equally effective in removing the partial volume averaging effect as the FLAIR diffusion imaging at a limited increase of the scan time since only a small number of b0 images needed to be obtained with the FLAIR contrast.     

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Vibration signal analysis is the most widely used technique in condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.     

Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images

PurposeSegmentation of the whole breast and fibroglandular tissue (FGT) is important for quantitatively analyzing the breast cancer risk in the dynamic contrast-enhanced magnetic resonance (DCE-MR) images. The purpose of this study is to improve the accuracy and efficiency of the segmentation of the whole breast and FGT in 3-D fat-suppressed DCE-MR images with a versatile deep learning (DL) framework.MethodsWe randomly collected 100 breast DCE-MR scans from Shanghai Cancer Hospital of Fudan University. The MR scans in the dataset were different in both the spatial resolution and the MR scanners employed. Furthermore, four breast density categories were assessed by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. The dataset was separated into the training and the testing sets, while keeping a balanced distribution of scans with different imaging parameters and density categories. The nnU-Net has been recently proposed to automatically adapt preprocessing strategies and network architectures for a given medical image dataset, thus showing a great potential in the systematic adaptation of DL methods to different datasets. In this study, we applied the nnU-Net to segment the whole breast and FGT in 3-D fat-suppressed DCE-MR images. Five-fold cross validation was employed to train and validate the segmentation method.ResultsThe segmentation performance was evaluated with the volume and surface agreement metrics between the DL-based automatic and the manually delineated masks, as quantified with the following measures: the average Dice volume overlap (0.968 ± 0.017 and 0.877 ± 0.081), the average surface distances (0.201 ± 0.080 mm and 0.310 ± 0.043 mm), and the Pearson correlation coefficient of masks (0.995 and 0.972) between the automatic and the manually delineated masks, as calculated for the whole breast and the FGT segmentation, respectively. The correlation coefficient between the breast densities obtained with the DL-based segmentation and the manual delineation was 0.981. There was a positive bias of 0.8% (DL-based relative to manual) in breast density measurement with the Bland-Altman plot. The execution time of the DL-based segmentation was approximately 20 s for the whole breast segmentation and 15 s for the FGT segmentation.ConclusionsOur DL-based segmentation framework using nnU-Net could robustly achieve high accuracy and efficiency across variable MR imaging settings without extra pre- or post-processing procedures. It would be useful for developing DCE-MR-based CAD systems to quantify breast cancer risk and to be integrated into the clinical workflow.     

Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling

Purpose

To investigate an effective time-resolved variable-density random undersampling scheme combined with an efficient parallel image reconstruction method for highly accelerated aortic 4D flow MR imaging with high reconstruction accuracy.

Materials and Methods

Variable-density Poisson-disk sampling (vPDS) was applied in both the phase-slice encoding plane and the temporal domain to accelerate the time-resolved 3D Cartesian acquisition of flow imaging. In order to generate an improved initial solution for the iterative self-consistent parallel imaging method (SPIRiT), a sample-selective view sharing reconstruction for time-resolved random undersampling (STIRRUP) was introduced. The performance of different undersampling and image reconstruction schemes were evaluated by retrospectively applying those to fully sampled data sets obtained from three healthy subjects and a flow phantom.

Results

Undersampling pattern based on the combination of time-resolved vPDS, the temporal sharing scheme STIRRUP, and parallel imaging SPIRiT, were able to achieve 6-fold accelerated 4D flow MRI with high accuracy using a small number of coils (N = 5). The normalized root mean square error between aorta flow waveforms obtained with the acceleration method and the fully sampled data in three healthy subjects was 0.04 ± 0.02, and the difference in peak-systolic mean velocity was − 0.29 ± 2.56 cm/s.

Conclusion

Qualitative and quantitative evaluation of our preliminary results demonstrate that time-resolved variable-density random sampling is efficient for highly accelerating 4D flow imaging while maintaining image reconstruction accuracy.     

An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior

Purpose

The objective of this paper was to automatically segment the cerebellum from T1-weighted human brain magnetic resonance (MR) images.

Materials and Methods

The proposed method constructs a cerebellum template using five sets of 3-T MR imaging (MRI) data, which are used to determine the initial position and the shape prior of the cerebellum for the active contour model. Our formulation includes the active contour model with shape prior, which thereby maintains the shape of the template. The proposed active contour model is sequentially applied to sagittal-, coronal- and transverse-view images. To evaluate the proposed method, it is applied to BrainWeb data and a 3-T MRI data set and compared with FreeSurfer with respect to performance assessment metrics.

Results

The segmented cerebellum was compared with the results from FreeSurfer. Using the manually segmented cerebellum as reference, we measured the average Jaccard coefficients of the proposed method, which were 0.882 and 0.885 for the BrainWeb data and 3-T MRI data set, respectively.

Conclusion

We presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. The proposed method yielded a robust and accurate segmentation result.     

Entrained droplets in underexpanded gas jet in water

Abstract  

A visualization study was performed to investigate the flow of an underexpanded nitrogen gas jet injected into water. The stagnation pressure was varied in the range 0.5–8.0 MPa. The gas jet length and expansion angle were obtained from time-averaged images captured using a high-speed camera. The gas jet length and expansion angle increased approximately linearly with increasing stagnation pressure. The entrainment velocity and the velocity of entrained water droplets in the gas jet were obtained by particle image velocimetry.     

Thermographic diagnosis system and imaging algorithm by distributed thermal data using single infrared sensor

An infrared diagnosis device provides two-dimensional images and patient-oriented results that can be easily understood by the inspection target by using infrared cameras. However, this device has disadvantages such as large size, high price, and inconvenient maintenance. In this regard, this study has proposed a small diagnosis device for body heat using a single infrared sensor and implementing an infrared detection system using a single infrared sensor and an algorithm that represents thermography using the obtained data on the temperature of the point source. The developed system had a temperature resolution of 0.1 °C and reproducibility of ±0.1 °C. The accuracy was 90.39% at the error bound of ±0 °C and 99.98% at that of ±0.1 °C. To evaluate the proposed algorithm and system, the infrared images of the camera method were compared. To verify the device’s clinical applicability, thermal images with clinical meaning were obtained from a patient who had lesions.     

Preparation and characterization of nickel hydroxide microspheres assembled by nanoribbons

Abstract  

This article describes a facile green strategy for preparing the Ni(OH)₂ microsphere assembled by nanoribbons by the template of the squama inner coat of onion at mild condition. The method is simple and effective. The results of SEM show the products are Ni(OH)₂ microspheres with diameters in the range of 500–2,500 nm. The results of TEM show the microsphere is assembly by nanoribbons, with thickness in the range of 30–50 nm. The products were characterized by powder X-ray diffraction, IR, and thermogravimetric analysis. A possible formation mechanism was also proposed.     

Automated segmentation of transcranial sonographic images in the diagnostics of Parkinson’s disease

Images captured during routine clinical transcranial sonography (TCS) examination are of a low resolution, so can be confusing for diagnostic evaluations. Manual segmentation of brain structures (areas of the midbrain and substantia nigra (SN)) that are of special interest cause inter-observer and intra-observer variability, thus restricting the reliability of Parkinson disease (PD) diagnostics. This paper presents a new technique for automated segmentation applicable to low resolution sonographic images, and particularly to brain structures related to PD. The segmentation was performed by a modified shape-based active contour (AC) segmentation algorithm. In order to suppress the speckle noise and to improve the AC segmentation, a pre-processing technique based on the averaging of adjusted spatially varying TCS images is proposed. The latter technique was tested on clinical TCS images. The results of the automated segmentation were compared with the manual markings. Two experts on the 40 TCS images performed these markings. The comparison showed that an automated method is effective when segmentation of the midbrain is performed (averaged overlap between regions obtained automatically and outlined manually was 73.10 ± 7.45%). The results of the segmentation of the SN area showed that a sufficiently correct contour of this area could also be obtained, but the accuracy of the segmentation is related to the image quality. It should be emphasised that the main difficulty in evaluating the accuracy of automated segmentation of the SN was the indefinite “gold standard” (variation between the measurements of two experts with different experience was found). And, therefore, the diagnostic reliability of the proposed technique was inconclusive.     

A robust graph-based segmentation method for breast tumors in ultrasound images

Objectives

This paper introduces a new graph-based method for segmenting breast tumors in US images.

Background and motivation

Segmentation for breast tumors in ultrasound (US) images is crucial for computer-aided diagnosis system, but it has always been a difficult task due to the defects inherent in the US images, such as speckles and low contrast.

Methods

The proposed segmentation algorithm constructed a graph using improved neighborhood models. In addition, taking advantages of local statistics, a new pair-wise region comparison predicate that was insensitive to noises was proposed to determine the mergence of any two of adjacent subregions.

Results and conclusion

Experimental results have shown that the proposed method could improve the segmentation accuracy by 1.5-5.6% in comparison with three often used segmentation methods, and should be capable of segmenting breast tumors in US images.     

Ultrasonic assisted synthesis of Bikitaite zeolite: A potential material for hydrogen storage application

Li containing Bikitaite zeolite has been synthesized by an ultrasound-assisted method and used as a potential material for hydrogen storage application. The Sonication energy was varied from 150 W to 250 W and irradiation time from 3 h to 6 h. The Bikitaite nanoparticles were characterized by X-ray diffraction (XRD), infrared (IR) spectral analysis, and field-emission scanning electron microscopy (FESEM) thermo-gravimetrical analysis and differential thermal analysis (TGA, DTA). XRD and IR results showed that phase pure, nano crystalline Bikitaite zeolites were started forming after 3 h irradiation and 72 h of aging with a sonication energy of 150 W and nano crystalline Bikitaite zeolite with prominent peaks were obtained after 6 h irradiation of 250 W sonic energy. The Brunauer–Emmett–Teller (BET) surface area of the powder by N₂ adsorption–desorption measurements was found to be 209 m²/g. The TEM micrograph and elemental analysis showed that desired atomic ratio of the zeolite was obtained after 6 h irradiation. For comparison, sonochemical method, followed by the hydrothermal method, with same initial sol composition was studied. The effect of ultrasonic energy and irradiation time showed that with increasing sonication energy, and sonication time phase formation was almost completed. The FESEM images revealed that 50 nm zeolite crystals were formed at room temperature. However, agglomerated particles having woollen ball like structure was obtained by sonochemical method followed by hydrothermal treatment at 100 °C for 24 h. The hydrogen adsorption capacity of Bikitaite zeolite with different Li content, has been investigated. Experimental results indicated that the hydrogen adsorption capacities were dominantly related to their surface areas as well as total pore volume of the zeolite. The hydrogen adsorption capacity of 143.2 c.c/g was obtained at 77 K and ambient pressure of (0.11 MPa) for the Bikitaite zeolite with 100% Li, which was higher than the reported values for other zeolites. To the best of our knowledge, there is no report on the synthesis of a Bikitaite zeolite by sonochemical method for H₂ storage.     

An improved novel joint channel estimation algorithm for the 112 Gbit/s PDM CO-OFDM system

In this paper, an improved novel joint channel estimation algorithm is proposed for the 112 Gbit/s PDM CO-OFDM system. This method combines the advantages of the intra-symbol frequency domain averaging (ISFA) method and the time domain averaging (TA) method, which can realize more accuracy channel estimation. Simulation results show that this proposal could promise a relatively stable performance even under a rapidly time varying environment. It significantly outperforms the ISFA method by 2.2 dB when the DGD equals 1000 ps, and has the advantages of 0.5 dB over ISFA in considering the laser linewidth and frequency offset.     

Micro-PIV measurement and CFD analysis of a thin liquid flow between rotating and stationary disks

Abstract  

A strongly sheared flow in a thin liquid layer between rotating and stationary disks is studied experimentally and numerically to clarify the characteristics of the flow in rotation-shearing chemical reactors. The disk diameter is 10 mm and the separation between the disks is 500 μm. The rotational speeds that are examined are 300, 500 and 700 rpm. The micro-PIV technique is used to measure the velocity in the liquid layer. A commercial CFD software is also used to obtain the results for the comparison and validation purposes. The overall velocity distributions revealed by the micro-PIV measurement are in good agreement with the CFD results. Both results show some interesting characteristics of the flow field, including the presence of a secondary flow and its influence on the tangential velocity profiles. The near-wall measurement in the micro-PIV technique is appreciably improved by the use of a simple digital, high-pass filtering technique that is applied to the acquired particle images. It is found that the flow characteristics in the thin liquid layer can be evaluated efficiently if the micro-PIV technique is used together with the high-pass filtering technique that is examined here.     

Rapid whole brain myelin water content mapping without an external water standard at 1.5 T

The objective of this study is to develop rapid whole brain mapping of myelin water content (MWC) at 1.5 T. The Fast Acquisition with Spiral Trajectory and T2prep (FAST-T2) pulse sequence originally developed for myelin water fraction (MWF) mapping was modified to obtain fast mapping of T1 and receiver coil sensitivity needed for MWC computation. The accuracy of the proposed T1 mapping was evaluated by comparing with the standard IR-FSE method. Numerical simulations were performed to assess the accuracy and reliability of the proposed MWC mapping. We also compared MWC values obtained with either cerebrospinal fluid (CSF) or an external water tube attached to the subject's head as the water reference. Our results from healthy volunteers show that whole brain MWC mapping is feasible in 7 min and provides accurate brain T1 values. Regional brain WC and MWC measurements obtained with the internal CSF-based water standard showed excellent correlation (R > 0.99) and negligible bias within narrow limits of agreement compared to those obtained with an external water standard.