Search

In this paper we present a simple method for flattening of triangulated surfaces for mapping and imaging. The method is based on classical results of F. Gehring and Y. Väisälä regarding the existence of quasi-conformal and quasi-isometric mappings between Riemannian manifolds. A random starting triangle version of the algorithm is presented. A curvature based version is also applicable. In addition the algorithm enables the user to compute the maximal distortion and dilatation errors. Moreover, the algorithm makes no use to derivatives, hence it is robust and suitable for analysis of noisy data. The algorithm is tested on data obtained from real CT images of the human brain cortex and colon, as well as on a synthetic model of the human skull. [ABSTRACT FROM AUTHOR]

Many computer aided diagnosis schemes in chest radiography start with preprocessing steps that try to remove or suppress normal anatomical structures from the image. Examples of normal structures in posteroanterior chest radiographs are bony structures. Removing these kinds of structures can be done quite effectively if the right dual energy images—two radiographic images from the same patient taken with different energies—are available. Subtracting these two radiographs gives a soft-tissue image with most of the rib and other bony structures removed. In general, however, dual energy images are not readily available. We propose a supervised learning technique for inferring a soft-tissue image from a standard radiograph without explicitly determining the additional dual energy image. The procedure, called dual energy faking, is based on k-nearest neighbor regression, and incorporates knowledge obtained from a training set of dual energy radiographs with their corresponding subtraction images for the construction of a soft-tissue image from a previously unseen single standard chest image. [ABSTRACT FROM AUTHOR]

With the emergence of swept-volume ultrasound (US) probes, precise and almost real-time US volume imaging has become available. This offers many new opportunities for computer guided diagnosis and therapy, 3-D images containing significantly more information than 2-D slices. However, computer guidance often requires knowledge about the exact position of US voxels relative to a tracking reference, which can only be achieved through probe calibration. In this paper we present a 3-D US probe calibration system based on a membrane phantom. The calibration matrix is retrieved by detection of a membrane plane in a dozen of US acquisitions of the phantom. Plane detection is robustly performed with the 2-D Hough transformation. The feature extraction process is fully automated, calibration requires about 20 minutes and the calibration system can be used in a clinical context. The precision of the system was evaluated to a root mean square (RMS) distance error of 1.15mm and to an RMS angular error of 0.61°. The point reconstruction accuracy was evaluated to 0.9mm and the angular reconstruction accuracy to 1.79°. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for classifying different tissue types in T1-weighted MR brain images using fuzzy segmentation. The main aim in this study is to compensate for the blurring effect on tissue boundaries due to partial volume effects. This paper is organized as follows: first, an adaptive greedy contour model has been developed to separate the intracranial volume (ICV) from the scalp and skull. Second, in order to deal with the problem of the partial volume effect, an algorithm for fuzzy segmentation is presented which has integrated fuzzy spatial affinity with statistical distributions of image intensities for each of the three tissues - cerebrospinal fluid, white matter and grey matter. This algorithm is tested on well-established simulated MR brain volumes to generate an extensive quantitative comparison with different noise levels and different slice thicknesses ranging from 1mm to 5mm. Finally, the results of this algorithm on clinical MR brain images are demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a semi-automatic method for tracking the mitral valve leaflet in transesophageal echocardiography. The algorithm requires a manual initialization and then segments an image sequence. The use of two constrained active contours and curve fitting techniques results in a fast segmentation algorithm. The active contours successfully track the inner cardiac muscle and the mitral valve leaflet axis. Three sequences have been processed and the generated muscle outline and leaflet axis have been visually assessed by an expert. This work is a part of a more general project which aims at providing real-time detection of the mitral valve leaflet in transesophageal echocardiography images. Keywords: Medical Image Analysis, Tracking and Motion, Active Contours, Ultrasound Imaging. [ABSTRACT FROM AUTHOR]

This paper addresses two questions concerning JPEG2000 compression - firstly - how much has noise influence on compression performance - secondly - can compression performance be improved by applying a new complementary conception with introducing a denoising process before the application of compression Indeed, radiographic images are a combination between the relevant signal and noise, which is per definition not compressible. The noise behaves generally close to a mixture of Gaussian and/or Poisson statistics, which generally affects the compression performance. In this paper, the influence of noise on the compression performance of JPEG2000 images with investigating the parameters signal dynamic and spatial pattern frequency are considered; and the JPEG2000 compression scheme combined with a denoising process is analyzed on simulated and real dental ortho-pan-tomographic images. The test images are generated using Poisson statistics; the denoising utilizes a Monte Carlo noise modeling method. A hundred selected images are denoised and the compression ratio, using lossless and lossy JPEG 2000, is reported and evaluated. [ABSTRACT FROM AUTHOR]

This paper presents an integrated algorithm for MRI (Magnetic Resonance Imaging) brain tissues segmentation. The method is composed of four stages. Noise in the MRI images is first reduced by a versatile wavelet-based filter. Then, the watershed algorithm is applied to brain tissues as an initial segmenting method. Because the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation. The third stage is a merging process for the over-segmentation regions using fuzzy clustering algorithm (Fuzzy C-Means). But there are still some regions which are not divided completely due to the low contrast in them, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. We exploited a method base on Minimum Covariance Determinant (MCD) estimator to detect the regions needed segmentation again, and then partition them by a supervised k-Nearest Neighbor (kNN) classifier. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments. [ABSTRACT FROM AUTHOR]

A novel approach is presented for modeling the complex activity pattern of the heart in echocardiogram videos. In this approach, the heart is represented by the constellation of its chambers, where the constellation is modeled by pictorial structure at each instance in time. Pictorial structure is then extended to the temporal domain to simultaneously capture the evolution pattern of the appearance of each chamber, the evolving spatial relationships between them, and the topological transformations in their constellation due to phase transitions. Inference and learning algorithms are presented for the model. The problem of correspondence is solved at each stage of the inference process, by matching the evolving model of the complex activity pattern to the observed constellations. The model, which is trained using examples of normal echocardiogram videos is shown to be efficient in temporal segmentation of the content of echocardiogram videos into different phases during one cycle of heart activity. [ABSTRACT FROM AUTHOR]

The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites' specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multi-photon microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive. [ABSTRACT FROM AUTHOR]

We introduce a novel minimally-interactive watershed algorithm that needs no initial parameterization, but lets the user refine the automatic segmentation close to real-time. In contrast to previous proposals, our algorithm encapsulates all time consuming calculation in a processing step executed only once. Thereby, a hierarchical subdivision of the incoming image data is generated. This subdivision serves as a basis for computing automatic segmentation results according to a given multi-dimensional classification scheme as well as for interactive refinement according to local markers. We have successfully applied our algorithm to efficiently removing bone structures from computed tomography angiography data, which is among the very challenging segmentation problems in medical image analysis. [ABSTRACT FROM AUTHOR]

Fluorescent confocal laser scanning microscope (CLSM) imaging has become popular in medical domain for the purpose of 3D information extraction. 3D information is extracted either by visual inspection or by automated techniques. Nonetheless, 3D information extraction from CLSM suffers from significant lateral intensity heterogeneity. We propose a novel lateral intensity heterogeneity correction technique to improve accurate image analysis, e.g., quantitative analysis, segmentation, or visualization. The proposed technique is novel in terms of its design (spatially adaptive mean-weight filtering) and application (CLSM), as well as its properties and full automation. The key properties of the intensity correction techniques include adjustment of intensity heterogeneity, preservation of fine structural details, and enhancement of image contrast. The full automation is achieved by data-driven parameter optimization and introduction of several evaluation metrics. We evaluated the performance by comparing with three other techniques, four quality metrics, and two realistic synthetic images and one real CLSM image. [ABSTRACT FROM AUTHOR]

We propose new techniques for unsupervised segmentation of multi-modal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of gray levels. We follow most conventional approaches such that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. Initial segmentation based on the LCG-models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments with medical images show that the proposed segmentation is more accurate than other known alternatives. [ABSTRACT FROM AUTHOR]

Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse). In principle, deformations based on B-splines are not invertible. The consistency term overcomes this limitation and allows registration of two images in a completely symmetric way. This extension of the elastic registration method simplifies the search for the optimum deformation and allows registering with no information about landmarks or deformation regularization. This approach can also be used as the first step to solve the problem of group-wise registration. ... [ABSTRACT FROM AUTHOR]

Prior knowledge about shape may be quite important for image segmentation. In particular, a number of different methods have been proposed to compute the statistics on a set of training shapes, which are then used for a given image segmentation task to provide the shape prior. In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding for doing shape analysis. The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. [ABSTRACT FROM AUTHOR]

Dynamic emission tomography is a technique used for quantifying the biochemical and physiological processes within the body. For certain neuroimaging applications, like kinetic modelling in positron emission tomography (PET), segmenting the measured data into a fewer number of regions-of-interest (ROI) is an important procedure needed for calculation of regional time-activity curves (TACs). Conventional estimation of regional activities in image domain suffers from substantial errors due to the reconstruction artifacts and segmentation inaccuracies. In this study, we present an approach for separating the dynamic tomographic data directly in the projection space using the least-squares method. Sinogram ROIs are the fractional parts of different tissue types measured at each voxel. Regional TACs can be estimated from the segmented sinogram ROIs, thereby avoiding the image reconstruction step. The introduced technique was validated with the two dynamic synthetic phantoms simulated based on ${^{11}\textrm{C}}$- and ${^{18}\textrm{F}}$-labelled tracer distributions. From the quantitative point of view, TAC estimation from the segmented sinograms yielded more accurate results compared to the image-based segmentation. [ABSTRACT FROM AUTHOR]

Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the local accuracy of image registration when no ground truth data is available. The technique is based on a statistical resampling technique called bootstrap. Only the two input images are used, no other data are needed. The general bootstrap uncertainty estimation framework described here is in principle applicable to most of the existing pixel based registration techniques. In practice, a large computing power is required. We present experimental results for a block matching method on an ultrasound image sequence for elastography with both known and unknown deformation field. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) and four-dimensional (4D) imaging of dynamic structures is a rapidly developing area of research in medical imaging. Non-rigid registration plays an important role for the analysis of these datasets. In this paper we will show some of the work of our group using non-rigid registration techniques for the detection of temporal changes such as growth in brain MR images. We will also show how non-rigid registration can be used to analyze the motion of the heart from cardiac MR images. [ABSTRACT FROM AUTHOR]

We describe a method to accurately assess articular cartilage morphology using the three-dimensional laser scanning technology. Traditional methods to obtain ground truth for validating the assessment of cartilage morphology from MR images have relied on water displacement, anatomical sections obtained with a high precision band saw, stereophotogrammetry, manual segmentation, and phantoms of known geometry. However, these methods are either limited to overall measurements such as volume and area, require an extensive setup and a highly skilled operator, or are prone to artifacts due to tissue sectioning. Alternatively, 3D laser scanning is an established technology that can provide high resolution range images of cartilage and bone surfaces. We present a method to extract these surfaces from scanned range images, register them spatially, and combine them into a single surface representing the articular cartilage from which volume, area, and thickness can be computed. We validated the laser scanning approach using a knee model which was covered with a synthetic articular cartilage model and compared the computed volume against water displacement measurements. Using this method, the volume of articular cartilage in five sets of cadaver knees was compared to volume estimates obtained from segmentation of MR images. [ABSTRACT FROM AUTHOR]

While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysis and computer-aided diagnosis (CAD). In this paper we investigate the performance of several state-of-the-art machine-learning methods on a CAD method for detecting prostatic adenocarcinoma from high resolution (4 Tesla) ex vivo MRI studies. A total of 14 different feature ensemble methods from 4 different families of ensemble methods were compared: Bayesian learning, Boosting, Bagging, and the k-Nearest Neighbor (kNN) classifier. Quantitative comparison of the methods was done on a total of 33 2D sections obtained from 5 different 3D MRI prostate studies. The tumor ground truth was determined on histologic sections and the regions manually mapped onto the corresponding individual MRI slices. All methods considered were found to be robust to changes in parameter settings and showed significantly less classification variability compared to inter-observer agreement among 5 experts. The kNN classifier was the best in terms of accuracy and ease of training, thus validating the principle of Occam's RazorOne should not increase, beyond what is necessary, the number of entities required to explain anything.. The success of a simple non-parametric classifier requiring minimal training is significant for medical image analysis applications where large amounts of training data are usually unavailable. [ABSTRACT FROM AUTHOR]

A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from 3D aortic MR images using segmentation and independent component analysis (ICA). The first step to extend the model to 4D (3D + time) has also been taken. ICA is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually. 3D+time MR image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta. The CAD method distinguished between normal and connective tissue disorder subjects with a classification accuracy of 93.5 %. [ABSTRACT FROM AUTHOR]

Malignant melanoma is the most deadly form of skin lesion. Early diagnosis is of critical importance to patient survival. Existent visual recognition algorithms for skin lesions classification focus mostly on segmentation and feature extraction. In this paper instead we put the emphasis on the learning process by using two kernel-based classifiers. We chose a discriminative approach using support vector machines, and a probabilistic approach using spin glass-Markov random fields. We benchmarked these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how performance changes by using color or textural features, and how it is affected by the quality of the segmentation mask. We show with extensive experiments that the support vector machine approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians. [ABSTRACT FROM AUTHOR]