Estimation of the deformations induced by articulated bodies: Registration of the spinal column

We present a new non-rigid registration algorithm estimating the displacement field generated by articulated bodies. Indeed the bony structures between different patient images may rigidly move while other tissues may deform in a more complex way. Our algorithm tracks the displacement induced in the column by a movement of the patient between two acquisitions. The volumetric deformation field in the whole body is then inferred from those displacements using a linear elastic biomechanical finite element model. We demonstrate in this paper that this method provides accurate results on 3D sets of computed tomography (CT), MR and positron emission tomography (PET) images and that the results of the registration algorithm show significant decreases in the mean, min and max errors.     

三维肺部图像中疑似肺结节结构的自动提取

本文提出了一种肺部CT图像三维数据中自动提取疑似结节区域的方法。首先结合阈值分割、种子填充等方法,在三维体数据上分割出肺实质。进而利用改进的模糊C均值聚类,提取出结节及具有结节特征的血管、支气管等感兴趣区域。该工作对感兴趣区域的特征提取有重要意义,是早期肺癌计算机辅助诊断重要的一步。     

Model-based particle picking for cryo-electron microscopy

We describe an algorithm for finding particle images in cryo-EM micrographs. The algorithm starts from a crude 3D map of the target particle, computed from a relatively small number of manually picked images, and then projects the map in many different directions to give synthetic 2D templates. The templates are clustered and averaged and then cross-correlated with the micrographs. A probabilistic model of the imaging process then scores cross-correlation peaks to produce the final picks. We give quantitative results on two quite different target particles: keyhole limpet hemocyanin and p97 AAA ATPase. On these particles our automatic particle picker shows human performance level, as measured by the Fourier shell correlations of 3D reconstructions.     

Visual computation of egomotion using an image interpolation technique

A novel technique is presented for the computation of the parameters of egomotion of a mobile device, such as a robot or a mechanical arm, equipped with two visual sensors. Each sensor captures a panoramic view of the environment. We show that the parameters of egomotion can be computed by interpolating the position of the image captured by one of the sensors at the robot's present location, with respect to the images captured by the two sensors at the robot's previous location. The algorithm delivers the distance travelled and angle rotated, without the explicit measurement or integration of velocity fields. The result is obtained in a single step, without any iteration or successive approximation. Tests of the algorithm on real and synthetic images reveal an accuracy to within 5% of the actual motion. Implementation of the algorithm on a mobile robot reveals that stepwise rotation and translation can be measured to within 10% accuracy in a three-dimensional world of unknown structure. The position and orientation of the robot at the end of a 30-step trajectory can be estimated with accuracies of 5% and 5°, respectively.     

Multiscale optical flow computation from the monogenic signal

We have developed an algorithm for the estimation of cardiac motion from medical images. The algorithm exploits monogenic signal theory, recently introduced as an N-dimensional generalization of the analytic signal. The displacement is computed locally by assuming the conservation of the monogenic phase over time. A local affine displacement model replaces the standard translation model to account for more complex motions as contraction/expansion and shear. A coarse-to-fine B-spline scheme allows a robust and effective computation of the models parameters and a pyramidal refinement scheme helps handle large motions. Robustness against noise is increased by replacing the standard pointwise computation of the monogenic orientation with a more robust least-squares orientation estimate. This paper reviews the results obtained on simulated cardiac images from different modalities, namely 2D and 3D cardiac ultrasound and tagged magnetic resonance. We also show how the proposed algorithm represents a valuable alternative to state-of-the-art algorithms in the respective fields.     

A real-time malleable molecular surface

We describe a method for generating a molecular surface using a parametric patch representation. Unlike previous methods, this algorithm generates a parametric patch surface which is smooth and G¹ continuous and manipulable in real-time. Crucial to our approach is the creation of a net of approximately equilateral triangles from which we generate the control points used as the basis for describing the surface. We present in detail the method used for generating the triangular net and accompanying control points, along with examples of the resulting surfaces.     

Integrating image quality in 2nu-SVM biometric match score fusion

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.     

An algorithm combining discrete and continuous methods for optical mapping.

Optical mapping is a novel technique for generating the restriction map of a DNA molecule by observing many single, partially digested copies of it, using fluorescence microscopy. The real-life problem is complicated by numerous factors: false positive and false negative cut observations, inaccurate location measurements, unknown orientations, and faulty molecules. We present an algorithm for solving the real-life problem. The algorithm combines continuous optimization and combinatorial algorithms applied to a nonuniform discretization of the data. We present encouraging results on real experimental data and on simulated data.     

First application of X-ray refraction-based computed tomography to a biomedical object

We have developed X-ray refraction-based computed tomography (CT) that is able to visualize soft tissue in between hard tissue. The experimental system consists of Si(220) diffraction double-crystals and is called the DEI (diffraction-enhanced imaging) method, in which the object is located between the crystals and a CCD camera to acquire data as 360 X-ray images. The X-ray energy used was 17.5 keV. The algorithm used to reconstruct CT images was developed by A. Maksimenko and colleagues. We successfully visualized articular cartilage and the distribution of bone marrow, which are inner structures. Our method has much higher contrast compared to the conventional absorption-based CT system.     

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer''s disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer''s Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer''s disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.     

基于SIFT特征和近似最近邻算法的医学CT图像检索

针对医学X线计算机断层（Computed Tomography,CT）图像,提出了一种基于尺度不变特征变换（Scale InvariantFeature Transform,SIFT）特征和近似最近邻算法的检索方法。首先通过SIFT算法得到图像的特征点和相应的特征向量,再采用近似最近邻算法进行SIFT特征向量的匹配搜索,得到数据库中与参考图像最相似的图像序列。实验结果表明,该法能检索到与目标图像细节相符的结果,大大提高了检索速度。与传统的基于纹理的检索方法相比,查准率和检索结果与目标图像的相似程度方面更佳,符合医学CT图像检索的要求。     

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.     

Response to visual threat following damage to the pulvinar

We present a unique case demonstrating contributions of the pulvinar in response to visual threat. Substantial evidence demonstrates that the amygdala contributes to the emotion of fear and the response to threat. Traditionally, two routes to amygdala activation have been distinguished: a "slow cortical" route through visual and association cortex and a "fast subcortical" route through the thalamus. The pulvinar nucleus of the thalamus is well connected to the amygdala, suggesting that pulvinar damage might interfere with amygdala activation and response to threat. We tested this possibility in patient SM, who suffered complete loss of the left pulvinar. We measured interference from threatening images on goal-directed behavior. In SM's ipsilesional field, threatening images slowed responses more than pleasant images did. This interference decreased rapidly over time. In contrast, in SM's contralesional field, interference from threatening images was initially absent and then increased rather than decreased over time. Processing through the pulvinar therefore plays a significant role in generating response to visual threat. We suggest that, with disruption of the subcortical route to the amygdala, briefly presented images were not fully processed for threat. The reemergence of interference over time may reflect contributions of a slower route.     

Constructing and counting phylogenetic invariants.

The method of invariants is an approach to the problem of reconstructing the phylogenetic tree of a collection of m taxa using nucleotide sequence data. Models for the respective probabilities of the 4m possible vectors of bases at a given site will have unknown parameters that describe the random mechanism by which substitution occurs along the branches of a putative phylogenetic tree. An invariant is a polynomial in these probabilities that, for a given phylogeny, is zero for all choices of the substitution mechanism parameters. If the invariant is typically non-zero for another phylogenetic tree, then estimates of the invariant can be used as evidence to support one phylogeny over another. Previous work of Evans and Speed showed that, for certain commonly used substitution models, the problem of finding a minimal generating set for the ideal of invariants can be reduced to the linear algebra problem of finding a basis for a certain lattice (that is, a free Z-module). They also conjectured that the cardinality of such a generating set can be computed using a simple "degrees of freedom" formula. We verify this conjecture. Along the way, we explain in detail how the observations of Evans and Speed lead to a simple, computationally feasible algorithm for constructing a minimal generating set.     

Application of bootstrap techniques to physical mapping

Ordering genetic markers or clones from a genomic library into a physical map is a central problem in genetics. In the presence of errors, there is no efficient algorithm known that solves this problem. Based on a standard heuristic algorithm for it, we present a method to construct a confidence neighborhood for a computed solution. We compute a confidence value for putative local solutions derived from bootstrap replicates of the original solution. In the reliable parts, the confidence neighborhood and the computed solution tend to coincide. In regions that are ill-defined by the data, the neighborhood contains additional reasonable alternatives. This offers the possibility of designing further experiments for the badly defined regions to improve the quality of the physical map. We analyze our approach by a simulation study and by application to a dataset of the genome of the bacterium Xylella fastidiosa.     

Optical-CT Dual-Modality Mapping Base on DRR Registration

Optical-CT dual-modality imaging requires the mapping between 2D fluorescence images and 3D body surface light flux. In this paper, we proposed an optical-CT dual-modality image mapping algorithm based on the Digitally Reconstructed Radiography (DRR) registration. In the process of registration, a series of DRR images were computed from CT data using the ray casting algorithm. Then, the improved HMNI similarity strategy based on Hausdorff distance was used to complete the registration of the white-light optical images and DRR virtual images. According to the corresponding relationship obtained by the image registration and the Lambert’s cosine law based on the pin-hole imaging model, the 3D light intensity distribution on the surface of the object could be solved. The feasibility and effectiveness of the mapping algorithm are verified by the irregular phantom and mouse experiments.     

Registration of fundus images for generating wide field composite images of the retina ]

The composition of retinal images presents high demands to the applied methods. Substantially different lighting conditions between the images, glarings and fade-outs within one image, large textureless regions and non-linear distortions are the main challenges. We present a fully automatic algorithm for the registration of images of the human retina and their overlay to wide field montage images combining area-based and point-based approaches. The algorithm combines an area-based as well as a point-based approach for determining similarities between images. Various measures of similarity were investigated, where the normalized correlation coefficient was superior compared to the usual definitions of transinformation. The transformation of the images was based on a quadratic model that can be derived from the spherical surface of the retina. This model was compared to four other parameterized transformations and performed best both visually and quantitatively in terms of measured misregistration. Problems may occur if the images are extremely defocused or contain very little relevant structural information.     

sFFT: a faster accurate computation of the p-value of the entropy score.

We present sFFT, an algorithm for efficiently computing the p-value of the information content, or the entropy score of an alignment of DNA sequences. Applying the FFT algorithm to an exponentially shifted probability mass function allows us perform fast convolutions that do not suffer from the otherwise overwhelming effect of accumulated numerical roundoff errors. Through a rigorous analysis of the propagation of numerical errors across the various steps of sFFT, we provide a theoretical bound on the overall error of our computed p-value. The accuracy of the computed p-value, as well as the utility of the error bound, are empirically demonstrated. Although there are faster algorithms that would compute this p-value, they can err significantly; sFFT is the fastest reliable algorithm. Finally, we note that the basic algorithm is likely to be applicable in a wider context than the one considered here.     

Fast space-filling molecular graphics using dynamic partitioning among parallel processors

We present a novel algorithm for the efficient generation of high-quality space-filling molecular graphics that is particularly appropriate for the creation of the large number of images needed in the animation of molecular dynamics. Each atom of the molecule is represented by a sphere of an appropriate radius, and the image of the sphere is constructed pixel-by-pixel using a generalization of the lighting model proposed by Porter (Comp. Graphics 1978, 12, 282). The edges of the spheres are antialiased, and intersections between spheres are handled through a simple blending algorithm that provides very smooth edges. We have implemented this algorithm on a multiprocessor computer using a procedure that dynamically repartitions the effort among the processors based on the CPU time used by each processor to create the previous image. This dynamic reallocation among processors automatically maximizes efficiency in the face of both the changing nature of the image from frame to frame and the shifting demands of the other programs running simultaneously on the same processors. We present data showing the efficiency of this multiprocessing algorithm as the number of processors is increased. The combination of the graphics and multiprocessor algorithms allows the fast generation of many high-quality images.     

A statistical approach to computer processing of cryo-electron microscope images: virion classification and 3-D reconstruction

The scattering density of the virus is represented as a truncated weighted sum of orthonormal basis functions in spherical coordinates, where the angular dependence of each basis function has icosahedral symmetry. A statistical model of the image formation process is proposed and the maximum likelihood estimation method computed by an expectation-maximization algorithm is used to estimate the weights in the sum and thereby compute a 3-D reconstruction of the virus particle. If multiple types of virus particle are represented in the boxed images then multiple 3-D reconstructions are computed simultaneously without first requiring that the type of particle shown in each boxed image be determined. Examples of the procedure are described for viruses with known structure: (1). 3-D reconstruction of Flockhouse Virus from experimental images, (2). 3-D reconstruction of the capsid of Nudaurelia Omega Capensis Virus from synthetic images, and (3). 3-D reconstruction of both the capsid and the procapsid of Nudaurelia Omega Capensis Virus from a mixture of unclassified synthetic images.