On the Mathematical Reconstruction of Two Dimensional Plants

A set of 10, chosen medicinal plants (some of them with a reputation as remedies for tuberculosis) has been investigated through Partitioned Iterated Function Systems-Semi Fractals with Angle (PIFS-SFA) coding, Lempel, Ziv, Welch with quantization and noise (LZW-QN) compression, and surface density statistics (f(α)-SDS) discrimination techniques. The final outcomes of this quantitative analysis were, firstly: the linear ordering of the plants in question accompanied by the hope that it reflects their medical significance, secondly: the mathematical representation of each of the plants, and thirdly: the impressive compression achieved, leading to remarkable computer memory saving, and still permitting successful pattern recognition i.e., proper identification of the plant from the compressed image.     

Image Alignment for Tomography Reconstruction from Synchrotron X-Ray Microscopic Images

A synchrotron X-ray microscope is a powerful imaging apparatus for taking high-resolution and high-contrast X-ray images of nanoscale objects. A sufficient number of X-ray projection images from different angles is required for constructing 3D volume images of an object. Because a synchrotron light source is immobile, a rotational object holder is required for tomography. At a resolution of 10 nm per pixel, the vibration of the holder caused by rotating the object cannot be disregarded if tomographic images are to be reconstructed accurately. This paper presents a computer method to compensate for the vibration of the rotational holder by aligning neighboring X-ray images. This alignment process involves two steps. The first step is to match the “projected feature points” in the sequence of images. The matched projected feature points in the - plane should form a set of sine-shaped loci. The second step is to fit the loci to a set of sine waves to compute the parameters required for alignment. The experimental results show that the proposed method outperforms two previously proposed methods, Xradia and SPIDER. The developed software system can be downloaded from the URL, http://www.cs.nctu.edu.tw/~chengchc/SCTA or http://goo.gl/s4AMx.     

Correlation between Lamina Cribrosa Tilt Angles,Myopia and Glaucoma Using OCT with a Wide Bandwidth Femtosecond Mode-Locked Laser

Purpose

To measure horizontal and vertical lamina cribrosa (LC) tilt angles and investigate associated factors using prototype optical coherence tomography (OCT) with a broad wavelength laser light source.

Design

Cross sectional study.

Methods

Twenty-eight no glaucoma eyes (from 15 subjects) and 25 glaucoma eyes (from 14 patients) were enrolled. A total of 300 optic nerve head B-scans were obtained in 10 µm steps and the inner edge of Bruch''s membrane opening (BMO) was identified as the reference plane. The vertical and horizontal angles between BMO line and approximate the best-fitting line for the surface of the LC were measured and potential associated factors were estimated with univariate and multivariate logistic regression analyses.

Results

The median (interquartile range) horizontal and vertical tilt angles were 7.10 (2.43–11.45) degrees and 4.15 (2.60–6.85) degrees in eyes without glaucoma and 8.50 (4.40–14.10) degrees and 9.30 (6.90–14.15) degrees in glaucoma eyes, respectively. The refractive errors had a statistically significant association with horizontal LC tilt angles (coefficients, −1.53 per diopter) and glaucoma had a significant correlation with vertical tilt angles (coefficients, 6.56) using multiple logistic regression analysis (p<0.001).

Conclusions

OCT allowed evaluation of the internal tilting of the LC compared with the BMO. The horizontal internal LC tilt angle was correlated with refractive errors, corresponding to myopic physiological changes, and vertical internal LC tilt was correlated with glaucoma, corresponding to glaucomatous pathological changes. These parameters have important implications for investigation of the correlation between myopia, glaucoma and LC morphological features.     

High-throughput Computer Method for 3D Neuronal Structure Reconstruction from the Image Stack of the Drosophila Brain and Its Applications

Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100 000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16 000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.     

基于VTK和MFC的医学图像三维重建研究与实现

VTK是医学可视化领域的主流工具,MFC是Windows平台下的应用程序框架。尝试将两者进行结合编程,以实现二维医学图像的三维重建。实现医学图像三维重建的主要方法是面绘制和体绘制。将利用多组医学图像数据进行三维重建研究,其中面绘制用移动立方体法,体绘制用光线投射法、最大密度投影法和合成体绘制法实现。最后比较两种绘制技术的结果并讨论了它们的特点。结果表明,VTK作为一种图像处理和三维可视化工具其功能是十分强大的。     

Three-Dimensional Reconstruction from Reduced Sets of Very Noisy Images Acquired Following a Single-Axis Tilt Schema: Application of a New Three-Dimensional Reconstruction Algorithm and Objective Comparison with Weighted Backprojection

In this work we propose a reconstruction algorithm (ART with blobs) that has not been previously used in electron Tomography and we compare it with the standard method in the field (weighted back projection, WBP). We assume that only a limited set of very noisy images, collected around a single axis tilt, is available; which is a typical situation in Electron Tomography. In general, the reconstruction problem is underdetermined (due to the limited number of projections) and the data are inconsistent (due to the high level of noise). The evaluation of the results is performed in a rigorous way by a task-oriented approach which makes use of numerical observers. ART with blobs outperforms WBP for a number of key tasks. Results are presented both for simplified line integral data and for realistic simulations of macromolecular structures embedded in amorphous ice.     

Reconstruction of 3D structures from protein contact maps

The prediction of the protein tertiary structure from solely its residue sequence (the so called Protein Folding Problem) is one of the most challenging problems in Structural Bioinformatics. We focus on the protein residue contact map. When this map is assigned it is possible to reconstruct the 3D structure of the protein backbone. The general problem of recovering a set of 3D coordinates consistent with some given contact map is known as a unit-disk-graph realization problem and it has been recently proven to be NP-Hard. In this paper we describe a heuristic method (COMAR) that is able to reconstruct with an unprecedented rate (3-15 seconds) a 3D model that exactly matches the target contact map of a protein. Working with a non-redundant set of 1760 proteins, we find that the scoring efficiency of finding a 3D model very close to the protein native structure depends on the threshold value adopted to compute the protein residue contact map. Contact maps whose threshold values range from 10 to 18 Ångstroms allow reconstructing 3D models that are very similar to the proteins native structure.     

Continuous Stroke Volume Estimation from Aortic Pressure Using Zero Dimensional Cardiovascular Model: Proof of Concept Study from Porcine Experiments

Introduction

Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation.

Method

This study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29–37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously.

Results

The median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5^th–95^th percentile) −12.4 ml–14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases.

Conclusion

The method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient.     

MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods

An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ₁-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ₁-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ₁-regularized parallel imaging methods.     

Automated correlation of single particle tilt pairs for Random Conical Tilt and Orthogonal Tilt Reconstructions

One of the major methodological challenges in single particle electron microscopy is obtaining initial reconstructions which represent the structural heterogeneity of the dataset. Random Conical Tilt and Orthogonal Tilt Reconstruction techniques in combination with 3D alignment and classification can be used to obtain initial low-resolution reconstructions which represent the full range of structural heterogeneity of the dataset. In order to achieve statistical significance, however, a large number of 3D reconstructions, and, in turn, a large number of tilted image pairs are required. The extraction of single particle tilted image pairs from micrographs can be tedious and time-consuming, as it requires intensive user input even for semi-automated approaches. To overcome the bottleneck of manual selection of a large number of tilt pairs, we developed an algorithm for the correlation of single particle images from tilted image pairs in a fully automated and user-independent manner. The algorithm reliably correlates correct pairs even from noisy micrographs. We further demonstrate the applicability of the algorithm by using it to obtain initial references both from negative stain and unstained cryo datasets.     

Image Quality in Children with Low-Radiation Chest CT Using Adaptive Statistical Iterative Reconstruction and Model-Based Iterative Reconstruction

Objective

To evaluate noise reduction and image quality improvement in low-radiation dose chest CT images in children using adaptive statistical iterative reconstruction (ASIR) and a full model-based iterative reconstruction (MBIR) algorithm.

Methods

Forty-five children (age ranging from 28 days to 6 years, median of 1.8 years) who received low-dose chest CT scans were included. Age-dependent noise index (NI) was used for acquisition. Images were retrospectively reconstructed using three methods: MBIR, 60% of ASIR and 40% of conventional filtered back-projection (FBP), and FBP. The subjective quality of the images was independently evaluated by two radiologists. Objective noises in the left ventricle (LV), muscle, fat, descending aorta and lung field at the layer with the largest cross-section area of LV were measured, with the region of interest about one fourth to half of the area of descending aorta. Optimized signal-to-noise ratio (SNR) was calculated.

Result

In terms of subjective quality, MBIR images were significantly better than ASIR and FBP in image noise and visibility of tiny structures, but blurred edges were observed. In terms of objective noise, MBIR and ASIR reconstruction decreased the image noise by 55.2% and 31.8%, respectively, for LV compared with FBP. Similarly, MBIR and ASIR reconstruction increased the SNR by 124.0% and 46.2%, respectively, compared with FBP.

Conclusion

Compared with FBP and ASIR, overall image quality and noise reduction were significantly improved by MBIR. MBIR image could reconstruct eligible chest CT images in children with lower radiation dose.     

Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) Improves Chest CT Image Quality and Reduces Radiation Exposure

Objective

To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT).

Methods

Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe''s test.

Results

At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.

Conclusion

For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.     

Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU), NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM) image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.     

Least Squares Estimation of Molecular Distance - Noise Abatement in Phylogenetic Reconstruction

Zuckerkandl and Pauling (1962, "Horizons in Biochemistry," pp. 189-225, Academic Press, New York) first noticed that the degree of sequence similarity between the proteins of different species could be used to estimate their phylogenetic relationship. Since then models have been developed to improve the accuracy of phylogenetic inferences based on amino acid or DNA sequences. Most of these models were designed to yield distance measures that are linear with time, on average. The reliability of phylogenetic reconstruction, however, depends on the variance of the distance measure in addition to its expectation. In this paper we show how the method of generalized least squares can be used to combine data types, each most informative at different points in time, into a single distance measure. This measure reconstructs phylogenies more accurately than existing non-likelihood distance measures. We illustrate the approach for a two-rate mutation model and demonstrate that its application provides more accurate phylogenetic reconstruction than do currently available analytical distance measures.     

Subjective Estimation of the Trajectory Length of Fused Auditory Image Movement

The perceived trajectory length of the movement of a fused auditory image (FAI) against the background of interaural intensity differences was estimated in eight subjects upon dichotic stimulation through headphones. FAI movement was created by gradually changing the interaural delay (ΔT) between clicks in binaurally presented series of clicks from 0 to ±630 μs. When the FAI moved from the left to the right, an increase in interaural intensity difference (ΔI) from 0 to +13 dB (in the right channel of stimulation) significantly shortened the FAI trajectory because of a greater lateralization of the start point of FAI movement as compared to its end point. In the case of an oppositely directed movement (from the right to the left), the movement trajectory significantly decreased when ΔI increased from 0 to −13 dB (i.e., in the left stimulation channel), which was also explained by the greater lateralization of the start point of FAI movement as compared to its end point.__________Translated from Fiziologiya Cheloveka, Vol. 31, No. 3, 2005, pp. 32–38.Original Russian Text Copyright © 2005 by Varyagina.     

Illumination Normalization of Face Image Based on Illuminant Direction Estimation and Improved Retinex

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.     

Estimation of Lipid Reserves in Unstained Living and Dead Nematodes by Image Analysis

During storage, non-feeding stages of entomopathogenic nematodes become visibly more transparent due to depletion of energy reserves. Optical density per unit area (OD per area) of infective juveniles of Steinernerna carpocapsae (All) and two Heterorhabditis isolates (UK211 and HF85) was measured with an image analysis system and compared with neutral lipid levels obtained by Oil Red O staining. Optical density (OD) measurements were compared with triglyceride levels of UK211 and HF85. Good correlations between OD per area and neutral lipids (0.90) and between OD and triglycerides (0.87) were found. Thus, OD reflects lipid levels and can be used as an indicator of lipid reserves in these nematodes. Heat-killing of nematodes had no significant effect on OD measurements, but length increased significantly. Storage in a triethanolamine in formaldehyde solution decreased the OD and OD/area by about 5% to 8%. An additional advantage of the image analysis method described is that repeated measurements can be performed on live nematodes. Key words: entomopathogenic nematode, Heterorhabditis, image analysis, neutral lipid, Oil Red O, optical density, Steinernema, triglyceride.     

Comparison of Cobb Angles Measured Manually,Calculated from 3-D Spinal Reconstruction,and Estimated from Torso Asymmetry

While scoliotic spinal deformity is traditionally measured by the Cobb angle, we seek to estimate scoliosis severity from the torso surface without X-ray radiation. Here, we measured the Cobb angle in three ways: by protractor from postero-anterior X-ray, by computer from a 3-D digitized model of the vertebral body line, and by neural-network estimation from indices of torso surface asymmetry. The estimates of the Cobb angle by computer and by neural network were equally accurate in 153 records from 52 patients (standard deviation of 6° from the Cobb angle, r =0.93 ), showing that torso asymmetry reliably predicted spinal deformity. Further improvements in predictive accuracy may require estimation of other 3-D indices of spinal deformity besides the Cobb angle with its wide measurement variability.     

电子显微三维重构技术发展与前沿

本文对电子显微三维重构技术(也称电镜三维重构,electron microscopy 3D reconstruction)进行简要介绍,并在此基础上对该技术当前研究的发展和前沿进行综述,包括高分辨率电镜三维重构、仪器设备性能突破、自动化数据收集和处理、高性能计算技术应用、二/三维图像处理技术的发展和创新、基于三维重构图的模型计算等方面,最后对电子显微三维重构技术的未来进行了展望。