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At least 253 records · Page 14Linked to original sources

PubMed2007

Bayesian parallel imaging with edge-preserving priors.

Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a nonconvex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.

Algorithms↗

PubMed1996

Segmentation of brain tissue from magnetic resonance images.

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. The brain is a particularly complex structure, and its segmentation is an important step for many problems, including studies in temporal change detection of morphology, and 3-D visualizations for surgical planning. We present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the computer vision literature: expectation/maximization segmentation, binary mathematical morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation such that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256 x 256 x 124 voxels and validate those results against segmentations generated by neuroanatomy experts.

Algorithms↗

PubMed2006

Object-of-interest image segmentation based on human attention and semantic region clustering.

We propose a novel object-of-interest (OOI) segmentation algorithm for various images that is based on human attention and semantic region clustering. As object-based image segmentation is beyond current computer vision techniques, the proposed method segments an image into regions, which are then merged as a semantic object. At the same time, an attention window (AW) is created based on the saliency map and saliency points from an image. Within the AW, a support vector machine is used to select the salient regions, which are then clustered into the OOI using the proposed region merging. Unlike other algorithms, the proposed method allows multiple OOIs to be segmented according to the saliency map.

Algorithms↗

PubMed2001

Efficient evaluation of serial sections by iterative Gabor matching.

Evaluation of electron microscopic images of serial sections is a time-consuming process requiring a high level of expertise. Here we present an algorithm to ease and accelerate this process. It is a modification of an algorithm successfully used in computer vision for object recognition. However, rather than recognising individual structures, we estimate the spatial mapping of a whole section onto the consecutive one. This mapping is used to transfer labelled information of the very first section, e.g. a classification by a human expert of different visible structures, onto structures visible in the next section. We investigate its performance on an artificially constructed benchmark as well as on real electron microscopic samples taken in primary visual cortex and demonstrate its potential for dramatically facilitating the evaluation process of serial sections.

Algorithms↗

PubMed2004

A maximum likelihood approach for image registration using control point and intensity.

Registration of multidate or multisensor images is an essential process in many image processing applications including remote sensing, medical image analysis, and computer vision. Control point (CP) and intensity are the two basic features used separately for image registration in the literature. In this paper, an exact maximum likelihood (EML) registration method, which combines both CP and intensity, is proposed for image alignment. The EML registration method maximizes the likelihood function based CP and intensity to estimate the registration parameters, including affine transformation and CP coordinates. The explicit formulas of the Cramer-Rao bound (CRB) are also derived for the proposed EML and conventional image registration algorithms. The performances of these image registration techniques are evaluated with the CRBs.

Algorithms↗

PubMed2005

Calibration of stereo cameras from two perpendicular planes.

We present a novel linear algorithm with which to calibrate stereo cameras from two perpendicular planes. Stereo cameras are two cameras aligned in a special configuration with coplanar image planes and parallel axes that are increasingly more widely used in computer vision tasks. Our objective is to present a more practical and simplified linear algorithm for these special configuration cameras, as traditional linear algorithms usually require too-strong constraints either on three-dimensional scenes or on the camera's motion. We developed the proposed algorithm from a new constraint by exploiting the orthogonality of two planes. The algorithm has much weaker constraints on three-dimensional scenes because two perpendicular planes are commonly found in daily life. We tested the algorithm with synthetic data and real image data. Experimental results show that it is both accurate and practical.

Algorithms↗

PubMed2006

New methods of MR image intensity standardization via generalized scale.

Image intensity standardization is a post-acquisition processing operation designed for correcting acquisition-to-acquisition signal intensity variations (non-standardness) inherent in Magnetic Resonance (MR) images. While existing standardization methods based on histogram landmarks have been shown to produce a significant gain in the similarity of resulting image intensities, their weakness is that in some instances the same histogram-based landmark may represent one tissue, while in other cases it may represent different tissues. This is often true for diseased or abnormal patient studies in which significant changes in image intensity characteristics may occur. In an attempt to overcome this problem, in this paper, we present two new intensity standardization methods based on two scale concepts developed in Madabhushi et al. [Computer Vision Image Understanding 101, 100-121 (2006)] for image processing applications. These scale concepts are utilized in this paper to accurately determine principal tissue regions within MR images. Landmarks derived from these regions are used to perform intensity standardization. The new methods were qualitatively and quantitatively evaluated on a total of 67 clinical three dimensional (3D) MR images corresponding to four different protocols and to normal, Multiple Sclerosis (MS), and brain tumor patient studies. The new scale-based methods were found to be better than the existing methods, with a significant improvement observed for severely diseased and abnormal patient studies.

Algorithms↗

PubMed2003

Informatics united: exemplary studies combining medical informatics, neuroinformatics and bioinformatics.

OBJECTIVES: Medical informatics, neuroinformatics and bioinformatics provide a wide spectrum of research. Here, we show the great potential of synergies between these research areas on the basis of four exemplary studies where techniques are transferred from one of the disciplines to the other. METHODS: Reviewing and analyzing exemplary and specific projects at the intersection of medical informatics, neuroinformatics, and bioinformatics from our experience in an interdisciplinary research group. RESULTS: Synergy emerges when techniques and solutions from medical informatics, bioinformatics, or neuroinformatics are successfully applied in one of the other disciplines. Synergy was found in 1. the modeling of neurophysiological systems for medical therapy development, 2. the use of image processing techniques from medical computer vision for the analysis of the dynamics of cell nuclei, and 3. the application of neuroinformatics tools for data mining in bioinformatics and as classifiers in clinical oncology. CONCLUSIONS: Each of the three different disciplines have delivered technologies that are readily applicable in the other disciplines. The mutual transfer of knowledge and techniques proved to increase efficiency and accuracy in a manifold of applications. In particular, we expect that clinical decision support systems based on techniques derived from neuro- and bioinformatics have the potential to improve medical diagnostics and will finally lead to a personalized delivery of healthcare.

Computational Biology↗

PubMed2004

Automatic identification of bacterial types using statistical imaging methods.

The objective of the current study is to develop an automatic tool to identify microbiological data types using computer-vision and statistical modeling techniques. Bacteriophage (phage) typing methods are used to identify and extract representative profiles of bacterial types out of species such as the Staphylococcus aureus. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes.

Bacteriophage Typing↗

PubMed2003

Computational models of oral and craniofacial development, growth, and repair.

This paper illustrates how biological and clinical problems stimulate research in biomedical informatics and how such research contributes to their solution. The computational models described use techniques from Logic Programming, Machine Learning, Computer Vision, and Biomathematics. They address problems in the development, growth, and repair of oral and craniofacial tissues arising in cell biology, clinical genetics, and dentistry. At the micro-level, the dynamic interaction of cells in the oral epithelium is modeled. At the macro-level, models are constructed of either the craniofacial shape of an individual or the craniofacial shape differences within and between healthy and congenitally abnormal populations. In between, in terms of scale, there are models of normal dentition and the use of computerized expert knowledge to guide the design of dental prostheses used to restore function in partially edentulous patients.

Adult↗

PubMed2005

Variational optical flow computation in real time.

This paper investigates the usefulness of bidirectional multigrid methods for variational optical flow computations. Although these numerical schemes are among the fastest methods for solving equation systems, they are rarely applied in the field of computer vision. We demonstrate how to employ those numerical methods for the treatment of variational optical flow formulations and show that the efficiency of this approach even allows for real-time performance on standard PCs. As a representative for variational optic flow methods, we consider the recently introduced combined local-global method. It can be considered as a noise-robust generalization of the Horn and Schunck technique. We present a decoupled, as well as a coupled, version of the classical Gauss-Seidel solver, and we develop several multgrid implementations based on a discretization coarse grid approximation. In contrast, with standard bidirectional multigrid algorithms, we take advantage of intergrid transfer operators that allow for nondyadic grid hierarchies. As a consequence, no restrictions concerning the image size or the number of traversed levels have to be imposed. In the experimental section, we juxtapose the developed multigrid schemes and demonstrate their superior performance when compared to unidirectional multgrid methods and nonhierachical solvers. For the well-known 316 x 252 Yosemite sequence, we succeeded in computing the complete set of dense flow fields in three quarters of a second on a 3.06-GHz Pentium4 PC. This corresponds to a frame rate of 18 flow fields per second which outperforms the widely-used Gauss-Seidel method by almost three orders of magnitude.

Algorithms↗

PubMed2004

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Algorithms↗

PubMed1993

Breast cytology diagnosis with digital image analysis.

An interactive computer system has been developed for evaluating cytologic features derived directly from a digital scan of breast fine needle aspirate slides. The system uses computer vision techniques to analyze cell nuclei and classifies them using an inductive method based on linear programming. A digital scan of selected areas of the aspirate slide is done by a trained observer, while the analysis of the digitized image is done by an untrained observer. When trained and tested on 119 breast fine needle aspirates (68 benign and 51 malignant) using leave-one-out testing, 90% correctness was achieved. These results indicate that the method is accurate (good intraobserver and interobserver reproducibility) and that an untrained operator can obtain diagnostic results comparable to those achieved visually by experienced observers.

Breast Neoplasms↗

PubMed2001

Multiphase method for automatic alignment of transmission electron microscope images using markers.

In order to successfully perform the 3D reconstruction in electron tomography, transmission electron microscope images must be accurately aligned or registered. So far, the problem is solved by either manually showing the corresponding fiducial markers from the set of images or automatically using simple correlation between the images on several rotations and scales. The present solutions, however, share the problem of being inefficient and/or inaccurate. We therefore propose a method in which the registration is automated using conventional colloidal gold particles as reference markers between images. We approach the problem from the computer vision viewpoint; hence, the alignment problem is divided into several subproblems: (1) finding initial matches from successive images, (2) estimating the epipolar geometry between consecutive images, (3) finding and localizing the gold particles with subpixel accuracy in each image, (4) predicting the probable matching gold particles using the epipolar constraint and its uncertainty, (5) matching and tracking the gold beads through the tilt series, and (6) optimizing the transformation parameters for the whole image set. The results show not only the reliability of the suggested method but also a high level of accuracy in alignment, since practically all the visible gold markers can be used.

Automation↗

PubMed2007

Direct curvature scale space: theory and corner detection.

The Curvature Scale Space (CSS) technique is considered to be a modern tool in image processing and computer vision. Direct Curvature Scale Space (DCSS) is defined as the CSS that results from convolving the curvature of a planar curve with a Gaussian kernel directly. In this paper we present a theoretical analysis of DCSS in detecting corners on planar curves. The scale space behavior of isolated single and double corner models is investigated and a number of model properties are specified which enable us to transform a DCSS image into a tree organization and, so that corners can be detected in a multiscale sense. To overcome the sensitivity of DCSS to noise, a hybrid strategy to apply CSS and DCSS is suggested.

Algorithms↗

PubMed2006

An experimental study on pedestrian classification.

Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular Support Vector Machines (SVMs), feed-forward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem.

Algorithms↗

PubMed2005

CT hepatic venography: 3D vascular segmentation for preoperative evaluation.

Preventing complications during hepatic surgery in living-donor transplantation or in oncologic resections requires a careful preoperative analysis of the hepatic venous anatomy. Such an analysis relies on CT hepatic venography data, which enhances the vascular structure due to contrast medium injection. However, a 3D investigation of the enhanced vascular anatomy based on typical computer vision tools is ineffective because of the large amount of occlusive opacities to be removed. This paper proposes an automated 3D approach for the segmentation of the vascular structure in CT hepatic venography, providing the appropriate tools for such an investigation. The developed methodology relies on advanced topological and morphological operators applied in mono- and multiresolution filtering schemes. It allows to discriminate the opacified vessels from the bone structures and liver parenchyma regardless of noise presence or inter-patient variability in contrast medium dispersion. The proposed approach was demonstrated at different phases of hepatic perfusion and is currently under extensive validation in clinical routine.

Algorithms↗

PubMed1993

Spatial, sequence-order-independent structural comparison of alpha/beta proteins: evolutionary implications.

We present a unique sequence-order independent approach which allows examination of three dimensional structures, searching for spatially similar substructural motifs. If the amino acids composing the motifs are contiguous in the primary chain, that is, they follow each other in the sequence, a common ancestor and a divergent evolutionary process may be implied. On the other hand, if the three-dimensional substructural motif consists of amino acids whose positions in the sequences vary between the different proteins, a convergent evolution might have taken place. Starting from different, ancient sequences, mutations may have occurred that brought about formation and conservation of a truly structural motif. Such a motif might be particularly suitable for fulfilling a specific function. Clearly, in order to be able to carry out such a task one needs a technique which allows comparisons of protein structures absolutely independent of their amino acid sequence-order. Our novel, efficient, computer vision based technique treats atoms (residues) as unconnected points in space, using strictly the atomic (either all atoms or only the C alpha atoms) coordinates. The order of the residues is completely disregarded. Detection, cataloging and analysis of "real" three-dimensional, sequence-order independent motifs in the crystallographic database is expected to be an invaluable tool for protein folding. Here we demonstrate the power of the technique by applying it to alpha/beta proteins. Our studies indicate that for some of the proteins, the "classical" structural alignments (conserving the amino acid order) are the optimal ones. Nevertheless, for others, truly spatial (out of sequential-order) amino acid equivalencing results in a better geometrical match.

Biological Evolution↗