Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation

In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.     

Robust pedestrian detection in thermal infrared imagery using the wavelet transform

A novel and robust pedestrian detection method in thermal infrared images based on the double-density dual-tree complex wavelet transform (DD-DT CWT) and wavelet entropy is presented in this paper. The regions of interest (ROIs) are located first making use of high brightness property of the pedestrian pixels caused by the self-emission of the pedestrians related to the Planck’s law. The candidate ROIs are then decomposed by DD-DT CWT and the wavelet entropy features are extracted from the high frequency subbands. The true pedestrian regions are finally classified and recognized using the support vector machine (SVM) classifier. Comparisons between our approach and traditional approaches are presented and experimental results using several thermal infrared image databases show the proposed scheme to be very promising.     

鸡胴体表面低可见污染物的双波段检测方法研究

提出一种基于高光谱成像技术的鸡胴体表面低可见污染物的双波段检测算法。首先,在所采集的高光谱数据中,选择ROI(region of interesting)内光谱的同一性最好、同时与边缘区域平均光谱差值最大的675 nm波段图像进行二值化处理,利用区域生长法提取最大连通区域作为掩膜。再将掩膜与污染物可分辨度最大的400 nm谱段图像进行“与”操作,提取出最大面积的鸡胴体待检ROI,最后利用标记法识别出ROI内有污染物存在的鸡胴体。试验结果表明,采用这种双波段算法,不仅可以获得能够根据鸡胴体形状及位置自适应调节的最大ROI(比已有研究方法提取的ROI面积大176%以上),而且对鸡胴体表面低可见度血液、胆汁和粪便的正确检出率平均可达81.6%。     

Prognosticating brain tumor patient survival after laser thermotherapy: Comparison between neuroradiological reading and semi-quantitative analysis of MRI data

Background and purposeGiven increasing interest in laser interstitial thermotherapy (LITT) to treat brain tumor patients, we explored if examining multiple MRI contrasts per brain tumor patient undergoing surgery can impact predictive accuracy of survival post-LITT.Materials and methodsMRI contrasts included fluid-attenuated inversion recovery (FLAIR), T1 pre-gadolinium (T1pre), T1 post-gadolinium (T1Gd), T2, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), susceptibility weighted images (SWI), and magnetization-prepared rapid gradient-echo (MPRAGE). The latter was used for MRI data registration across preoperative to postoperative scans. Two ROIs were identified by thresholding preoperative FLAIR (large ROI) and T1Gd (small ROI) images. For each MRI contrast, a numerical score was assigned based on changing image intensity of both ROIs (vs. a normal ROI) from preoperative to postoperative stages. The fully-quantitative method was based on changing image intensity across scans at different stages without any human intervention, whereas the semi-quantitative method was based on subjective criteria of cumulative trends across scans at different stages. A fully-quantitative/semi-quantitative score per patient was obtained by averaging scores for each MRI contrast. A standard neuroradiological reading score per patient was obtained from radiological interpretation of MRI data. Scores from all 3 methods per patient were compared against patient survival, and re-examined for comorbidity and pathology effects.ResultsPatient survival correlated best with semi-quantitative scores obtained from T1Gd, ADC, and T2 data, and these correlations improved when biopsy and comorbidity were included.ConclusionThese results suggest interfacing neuroradiological readings with semi-quantitative image analysis can improve predictive accuracy of patient survival.     

中药光谱成像图像自适应区域增长分割方法

为了消除背景噪声对药材光谱图像检测结果的干扰,根据中药材光谱图像的特点,设计一种能够自适应对中药材光谱图像进行有效区域(ROI)分割的区域增长算法。该区域增长算法根据药材光谱图像的灰度直方图分布来自动选取种子点和分割阈值,在生长的同时进行连通性分析,生长结束后通过区域填充技术来消除图像中出现的孔洞。实验表明:该方法能够自动、准确地进行ROI分割,分割偏差小于8%,并且能较好地消除噪声的干扰,没有产生无意义的生长区域。     

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper,we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the preprocessing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.     

Evaluation of atlas-based segmentation of hippocampi in healthy humans

Introduction and aim

Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.

Material and methods

High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/).

Results

The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas.

Conclusion

Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.     

Multilevel region of interest guided bit allocation for multiview video coding

Multiview video coding (MVC) is an efficient compression scheme, by which the large amount of multiview video data can be effectively processed. However, it ignores the characteristics of the human visual system. In this paper, we propose a multilevel region of interest (ROI) based bit allocation strategy for MVC, which can take advantage of the visual redundancy to improve encoding efficiency. First, the macro block (MB) saliency is derived from depth information of the video sequence, and the multilevel ROI segmentation is conducted based on the MB saliency distribution. Then, the multiview video bit allocation strategy based on multilevel ROI is proposed. We have evaluated system performance with several multiview video sequences in JMVC 8.5 reference software. Experimental results show that the quality of ROIs has a considerable improvement when the bit rate consumed is kept consistent with JMVC. Meanwhile, the proposed MVC method can save bit rate while maintaining the overall image quality.     

基于直方图的感兴趣区图像边缘检测

当图像中的某些区域具有比其它部分更高的重要性时,基于感兴趣区域(ROI)的边缘检测功能就尤为重要。但是当前大部分算法均针对图像整体检测,这在一定程度上会影响对ROI区域的检测效果。针对该问题,提出一种基于ROI的边缘检测算法。新算法首先利用修正后的图像直方图特征选取ROI区域的分割阈值,然后根据分割阈值从背景中分离出ROI区域,最后选择最优边缘检测算子,完成基于ROI的边缘检测。实验结果表明:新算法能够更好的支持对ROI区域的边缘抽取。     

Reproducibility of myelin water fraction analysis: a comparison of region of interest and voxel-based analysis methods

This study compared region of interest (ROI) and voxel-based analysis (VBA) methods to determine the optimal method of myelin water fraction (MWF) analysis. Twenty healthy controls were scanned twice using a multi-echo T₂ relaxation sequence and ROIs were drawn in white and grey matter. MWF was defined as the fractional signal from 15 to 40 ms in the T₂ distribution. For ROI analysis, the mean intensity of voxels within an ROI was fit using non-negative least squares. For VBA, MWF was obtained for each voxel and the mean and median values within an ROI were calculated. There was a slightly higher correlation between Scan 1 and 2 for the VBA method (R²=0.98) relative to the ROI method (R²=0.95), and the VBA mean square difference between scans was 300% lower, indicating VBA was the most consistent between scans. For the VBA method, mean MWF was found to be more reproducible than median MWF. As the VBA method is more reproducible and gives more options for visualization and analysis of MWF, it is recommended over the ROI method of MWF analysis.     

基于感兴趣区域的海洋卫星遥感图像压缩算法

针对海洋监视卫星遥感图像的特点,提出了一种基于感兴趣区域的自适应遥感图像近无损压缩算法。该算法利用红-黑小波提升方法检测出目标的边缘,采用阈值法将目标和背景分割,使用外接矩形来描述目标,从而自动确定感兴趣区域。感兴趣区域采用Rice无损熵编码方法,背景区域采用比特平面编码的有损压缩方法。实验结果表明,该算法能有效地划分感兴趣区域,其压缩比较无损压缩有了很大提高,目标无失真,整幅图像的峰值信噪比都在40dB以上。算法的计算复杂度较低,易于硬件实现,而且还具有自适应性和数据包独立的优点,适合于海洋监视卫星遥感图像近无损压缩应用。     

Upconversion,avalanche effect and controlled optical switching in Yb3+, Ho3+ co-doped Ca12Al14O33 phosphor

In the present work, Yb³⁺/Ho³⁺ co-doped Ca₁₂Al₁₄O₃₃ phosphor has been synthesized and upconversion (UC) fluorescence has been studied by pumping the sample with 976 nm radiation. In addition to a strong UC fluorescence due to Ho³⁺, an avalanche effect is also observed with an increase in input laser power. At a certain threshold power, optical switching (a switching in emission intensity of different transitions) behavior is observed whose response time has been estimated to be of the order of milliseconds. A substantial heat generation inside the host matrix due to electron–phonon coupling (non-radiative relaxations) of the rare-earth ion and the host matrix has been assigned responsible for the switching phenomenon. The heat generation has been confirmed and estimated using fluorescence intensity ratio (FIR) method.     

A new method for measuring the threshold of stimulated Brillouin scattering

A new method for measuring the threshold of stimulated Brillouin scattering (SBS) based on the generation location of a Stokes beam is proposed for the first time to our knowledge. The length of the medium cell is selected to be longer than the free gain length of pump pulses in the Brillouin medium. The reflected light from a certain mirror in front of the medium cell is chosen as the reference beam, and the SBS threshold is measured by the "jump" of the delay between the Stokes beam and the reference beam. An 8-ns Q-switched single-longitudinal-mode pulse is used as the pump and the typical SBS medium FC-72 is selected as the nonlinear medium in our experiment. The SBS threshold intensity is measured to be 173-178 mW/cm 2 , which is consistent with existing results measured with the transmitted energy limiting method.     

基于ROI编码的干涉超光谱图像近无损压缩

针对干涉超光谱成像仪所成图像的特点,提出了一种基于ROI（Region of Interest,感兴趣区域）编码的干涉超光谱图像序列压缩方法.利用帧内差值变换,结合矩形ROI编码,在提高遥感图像压缩比的同时,使压缩后的重建图像质量达到近无损的技术要求.实验结果表明,这种压缩方法在干涉超光谱图像压缩中当相对光谱二次误差为2.04％时,干涉图像的压缩比达到9.66∶1.     

Early sinkhole detection using a drone-based thermal camera and image processing

Accurate advance detection of the sinkholes that are occurring more frequently now is an important way of preventing human fatalities and property damage. Unlike naturally occurring sinkholes, human-induced ones in urban areas are typically due to groundwater disturbances and leaks of water and sewage caused by large-scale construction. Although many sinkhole detection methods have been developed, it is still difficult to predict sinkholes that occur in depth areas. In addition, conventional methods are inappropriate for scanning a large area because of their high cost. Therefore, this paper uses a drone combined with a thermal far-infrared (FIR) camera to detect potential sinkholes over a large area based on computer vision and pattern classification techniques.To make a standard dataset, we dug eight holes of depths 0.5–2 m in increments of 0.5 m and with a maximum width of 1 m. We filmed these using the drone-based FIR camera at a height of 50 m. We first detect candidate regions by analysing cold spots in the thermal images based on the fact that a sinkhole typically has a lower thermal energy than its background. Then, these regions are classified into sinkhole and non-sinkhole classes using a pattern classifier. In this study, we ensemble the classification results based on a light convolutional neural network (CNN) and those based on a Boosted Random Forest (BRF) with handcrafted features. We apply the proposed ensemble method successfully to sinkhole data for various sizes and depths in different environments, and prove that the CNN ensemble and the BRF one with handcrafted features are better at detecting sinkholes than other classifiers or standalone CNN.     

Composite and classified color display in MR imaging of the female pelvis.

Because of its superior soft-tissue-imaging capabilities, MRI has proved to be an excellent modality for visualizing the contents of the female pelvis. In an effort to potentially improve gynecological MRI studies, we have applied color composite techniques to sets of spin-echo and gradient-echo gray-tone MR images obtained from various individuals. For composite generation, based on tissue region of interest calculated mean pixel intensity values, various colors were applied to spatially aligned images using a DEC MicroVAX II computer with interactive digital language (IDL) so that tissue contrast patterns could be optimized in the final image. The IDL procedures, which are similar to those used in NASA's LANDSAT image processing system, allowed the generation of single composite images displaying the combined information present in a series of spatially aligned images acquired using different pulse sequences. With our composite generation techniques, it was possible to generate seminatural-appearing color images of the female pelvis that possessed enhanced conspicuity of specific tissues and fluids. For comparison with color composites, classified images were also generated based on computer recognition and statistical separation of distinct tissue intensity patterns in an image set using the maximum likelihood processing algorithm.     

基于有限脉冲响应滤波器的数字全息零级像消除

针对数字全息中零级像的存在影响数字再现像的质量,分析数字全息图的记录、再现原理及频谱特性,提出了一种利用有限脉冲响应滤波器消除全息图数字再现中零级像的方法.该方法只需记录一幅数字全息图,不需要相移器材或其他辅助设备,直接利用数字图像处理对数字全息图在空域进行预处理,消除全息再现时的零级像干扰.对比了数字模拟和实验拍摄到的全息图在应用有限脉冲响应滤波器消除零级像前后的再现结果,表明该方法可消除零级衍射像,改善重建像质量,并且算法简单.     

Detection of pedestrians in far-infrared automotive night vision using region-growing and clothing distortion compensation

We present a night-time pedestrian detection system based on automotive infrared video processing. Far-infrared or thermal night vision is a technology well suited for automatic detection of pedestrians at night as they generally appear warmer than the background. However, the appearance of a pedestrian in IR video can vary dramatically depending on the physical properties of the clothing they wear, the time spent adjusting to the outside environment, and the ambient temperature. We highlight the difficulties of detection in low temperatures (below 8 °C) when pedestrians typically wear highly insulating clothing, which can lead to distortion of the IR signature of the pedestrian. A pre-processing step is presented, which compensates for this clothing-based distortion using vertically-biased morphological closing. Potential pedestrians (Regions of Interest) are then segmented using feature-based region-growing with high intensity seeds. Histogram of Oriented Gradients (HOG) features are extracted from candidates and utilised for Support Vector Machine classification. Positively classified targets are tracked between frames using a Kalman filter, adding robustness and increasing performance. The proposed system adapts not just to variations between images or video frames, but to variations in appearance between different pedestrians in the same image or frame. Results indicate improved performance compared to previous HOG–SVM automotive IR pedestrian detection systems, which utilised stereo IR cameras.     

Geometric distortion correction for echo planar images using nonrigid registration with spatially varying scale

One method used to correct geometric and intensity distortions in echo planar images is to register them to undistorted images via nonrigid deformations. However, some areas in the echo planar images are more distorted than others, thus suggesting the use of deformations whose characteristics are adapted spatially. In this article, we incorporate into our previously developed registration algorithm a spatially varying scale mechanism, which adapts the local scale properties of the transformation by means of a scale map. To compute the scale map, a technique is proposed that relies on an estimate of the expected deformation field. This estimate is generated using knowledge of the physical processes that induce distortions in echo planar images. We evaluate the method of spatially varying scale on both simulated and real data. We find that, in comparison with our earlier method using fixed scale, our new method finds deformation fields that are smoother and finds them faster without sacrificing accuracy.     

一种干涉高光谱图像的3DSPIHT结合ROI压缩算法

 针对大孔径静态干涉成像光谱仪的成像特点,提出了一种基于三维小波变换的3DSPIHT算法结合ROI的图像压缩方案.对干涉高光谱图像序列进行了三维非对称离散小波变换.采用ROI方法对主要的光谱系数进行提升,以保护光谱信息.最后,对3DSPIHT算法进行改进,以有效编码干涉高光谱图像的小波变换系数.实验结果表明,该方法在8:1压缩比下可获得大于40 dB的峰值信噪比,同时有效地保护了光谱信息.