Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction

Cerebral microvascular network geometry changes in response to functional stimulation

The cortical microvessels are organized in an intricate, hierarchical, three-dimensional network. Superimposed on this anatomical complexity is the highly complicated signaling that drives the focal blood flow adjustments following a rise in the activity of surrounding neurons. The microvascular response to neuronal activation remains incompletely understood. We developed a custom two photon fluorescence microscopy acquisition and analysis to obtain 3D maps of neuronal activation-induced changes in the geometry of the microvascular network of the primary somatosensory cortex of anesthetized rats. An automated, model-based tracking algorithm was employed to reconstruct the 3D microvascular topology and represent it as a graph. The changes in the geometry of this network were then tracked, over time, in the course of electrical stimulation of the contralateral forepaw. Both dilatory and constrictory responses were observed across the network. Early dilatory and late constrictory responses propagated from deeper to more superficial cortical layers while the response of the vertices that showed initial constriction followed by later dilation spread from cortical surface toward increasing cortical depths. Overall, larger caliber adjustments were observed deeper inside the cortex. This work yields the first characterization of the spatiotemporal pattern of geometric changes on the level of the cortical microvascular network as a whole and provides the basis for bottom-up modeling of the hemodynamically-weighted neuroimaging signals.

Model generation of coronary artery bifurcations from CTA and single plane angiography

PURPOSE

To generate accurate and realistic models of coronary artery bifurcations before and after percutaneous coronary intervention (PCI), using information from two image modalities. Because bifurcations are regions where atherosclerotic plaque appears frequently and intervention is more challenging, generation of such realistic models could be of high value to predict the risk of restenosis or thrombosis after stent implantation, and to study geometrical and hemodynamical changes.

METHODS

Two image modalities have been employed to generate the bifurcation models: computer tomography angiography (CTA) to obtain the 3D trajectory of vessels, and 2D conventional coronary angiography (CCA) to obtain radius information of the vessel lumen, due to its better contrast and image resolution. In addition, CCA can be acquired right before and after the intervention in the operation room; therefore, the combination of CTA and CCA allows the generation of realistic preprocedure and postprocedure models of coronary bifurcations. The method proposed is semiautomatic, based on landmarks manually placed on both image modalities.

RESULTS

A comparative study of the models obtained with the proposed method with models manually obtained using only CTA, shows more reliable results when both modalities are used together. The authors show that using preprocedure CTA and postprocedure CCA, realistic postprocedure models can be obtained. Analysis carried out of the Murray's law in all patient bifurcations shows the geometric improvement of PCI in our models, better than using manual models from CTA alone. An experiment using a cardiac phantom also shows the feasibility of the proposed method.

CONCLUSIONS

The authors have shown that fusion of CTA and CCA is feasible for realistic generation of coronary bifurcation models before and after PCI. The method proposed is efficient, and relies on minimal user interaction, and therefore is of high value to study geometric and hemodynamic changes of treated patients.

A stochastic model for automatic extraction of 3D neuronal morphology

Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked Point Process model enables unsupervised network extraction by fitting a configuration of objects with globally optimal associated energy to the centreline of the arbors. For this purpose we propose special configurations of marked objects and an energy function well adapted for detection of 3D tubular branches. The optimization of the energy function is achieved by a stochastic, discrete-time multiple birth and death dynamics. Our method finds the centreline, local width and orientation of neuronal arbors and identifies critical nodes like bifurcations and terminals. The proposed model is tested on 3D light microscopy images from the DIADEM data set with promising results.

DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS

This paper investigates a diffeomorphic point-set registration based on non-stationary mixture models. The goal is to improve the non-linear registration of anatomical structures by representing each point as a general non-stationary kernel that provides information about the shape of that point. Our framework generalizes work done by others that use stationary models. We achieve this by integrating the shape at each point when calculating the point-set similarity and transforming it according to the calculated deformation. We also restrict the non-rigid transform to the space of symmetric diffeomorphisms. Our algorithm is validated in synthetic and human datasets in two different applications: fiber bundle and lung airways registration. Our results shows that non-stationary mixture models are superior to Gaussian mixture models and methods that do not take into account the shape of each point.

Coronary artery center-line extraction using second order local features

Of interest is the accurate and robust delineation of vessel center-lines for complete arterial tree structure in coronary angiograms which is an imperative step towards 3D reconstruction of coronary tree and feature-based registration of multiple view angiograms. Most existing center-line tracking methods encounter limitations in coping with abrupt variations in local artery direction and sudden changes of lumen diameter that occur in the vicinity of arterial lesions. This paper presents an improved center-line tracing algorithm for automatic extraction of coronary arterial tree based on robust local features. The algorithm employs an improved scanning schema based on eigenvalues of Hessian matrix for reliable identification of true vessel points as well as an adaptive look-ahead distance schema for calculating the magnitude of scanning profile. In addition to a huge variety of clinical examples, a well-established vessel simulation tool was used to create several synthetic angiograms for objective comparison and performance evaluation. The experimental results on the accuracy and robustness of the proposed algorithm and its counterparts under difficult situations such as poor image quality and complicated vessel geometry are presented.

Automated delineation of calcified vessels in mammography by tracking with uncertainty and graphical linking techniques

As a potential biomarker for women's cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular structures with calcium depositions in mammography, a new computerized method is introduced in this paper to delineate the calcified vessels. Specifically, we leverage two underlying cues, namely calcification and vesselness, into a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates plenty of sampling paths to describe the complicated topology of the vascular structures with calcium depositions. A compiling and linking process is further carried out to organize the sampling paths together to be the vessel segments that likely belong to the same vessel tract. The proposed method has been evaluated on 63 mammograms, by comparison with manual delineations from two experts using various assessment metrics. The experiment results confirm the efficacy and stability of the proposed method, and also indicate that the proposed method can be potentially used as a convenient BAC measurement tool in replacement of the trivial and tedious manual delineation tasks.

Computer-aided analysis of airway trees in micro-CT scans of ex vivo porcine lung tissue

We present a highly automated approach to obtain detailed structural models of airway trees from ex vivo porcine lung tissue imaged with a high resolution micro-CT scanner. Such information is an important prerequisite to systematically study models of lung disease that affect airway morphology. The method initially identifies all tubular airway-like structures in the lung. In a second processing step, these structures are grouped into a connected airway tree by utilizing prior knowledge about the airway trees branching pattern. The method was evaluated on 12 micro-CT scans from four tracheal lobes of piglets imaged at three different inflation levels. For this study, two control piglets and two cystic fibrosis piglets were used. For systematic validation of our approach, an airway nomenclature was developed for the pig airway tree. Out of more than 3500 airway tree segments assessed during evaluation, 88.45% were correctly identified by the method. No false positive airway branches were found. A detailed performance analysis for different airway tree hierarchy levels, lung inflation levels and piglets with/without cystic fibrosis is presented in the paper.

Mapping microvasculature with acoustic angiography yields quantifiable differences between healthy and tumor-bearing tissue volumes in a rodent model

PURPOSE

To determine if the morphologies of microvessels could be extracted from contrast material-enhanced acoustic angiographic ultrasonographic (US) images and used as a quantitative basis for distinguishing healthy from diseased tissue.

MATERIALS AND METHODS

All studies were institutional animal care and use committee approved. Three-dimensional contrast-enhanced acoustic angiographic images were acquired in both healthy (n = 7) and tumor-bearing (n = 10) rats. High-spatial-resolution and high signal-to-noise acquisition was enabled by using a prototype dual-frequency US transducer (transmit at 4 MHz, receive at 30 MHz). A segmentation algorithm was utilized to extract microvessel structure from image data, and the distance metric (DM) and the sum of angles metric (SOAM), designed to distinguish different types of tortuosity, were applied to image data. The vessel populations extracted from tumor-bearing tissue volumes were compared against vessels extracted from tissue volumes in the same anatomic location within healthy control animals by using the two-sided Student t test.

RESULTS

Metrics of microvascular tortuosity were significantly higher in the tumor population. The average DM of the tumor population (1.34 ± 0.40 [standard deviation]) was 23.76% higher than that of the control population (1.08 ± 0.08) (P < .0001), while the average SOAM (22.53 ± 7.82) was 50.73% higher than that of the control population (14.95 ± 4.83) (P < .0001). The DM and SOAM metrics for the control and tumor populations were significantly different when all vessels were pooled between the two animal populations. In addition, each animal in the tumor population had significantly different DM and SOAM metrics relative to the control population (P < .05 for all; P value ranges for DM, 3.89 × 10(-)(7) to 5.63 × 10(-)(3); and those for SOAM, 2.42 × 10(-)(12) to 1.57 × 10(-)(3)).

CONCLUSION

Vascular network quantification by using high-spatial-resolution acoustic angiographic images is feasible. Data suggest that the angiogenic processes associated with tumor development in the models studied result in higher instances of vessel tortuosity near the tumor site.

A broadly applicable 3-D neuron tracing method based on open-curve snake

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

NeuronGrowth, a software for automatic quantification of neurite and filopodial dynamics from time-lapse sequences of digital images

We developed NeuronGrowth, a software for the automatic quantification of extension and retraction of neurites and filopodia, from time-lapse sequences of two-dimensional digital micrographs. NeuronGrowth requires a semiautomatic characterization of individual neurites in a reference frame, which is then used for automatic tracking and measurement of every neurite over the whole image sequence. Modules for sequence alignment, background subtraction, flat field correction, light normalization, and cropping have been integrated to improve the quality of the analysis. Moreover, NeuronGrowth incorporates a deconvolution filter that corrects the shadow-cast effect of differential interference contrast (DIC) images. NeuronGrowth was tested by analyzing the formation of outgrowth patterns by individual leech neurons cultured under two different conditions. Phase contrast images were obtained from neurons plated on CNS homogenates and DIC images were obtained from similar neurons plated on ganglion capsules as substrates. Filopodia were measured from fluorescent growth-cones of chick dorsal root ganglion cells. Quantitative data of neurite extension and retraction obtained by three different users applying NeuronGrowth and two other manually operated software packages were similar. However, NeuronGrowth required less user participation and had a better time performance when compared with the other software packages. NeuronGrowth may be used in general to quantify the dynamics of tubular structures such as blood vessels. NeuronGrowth is a free plug-in for the free software ImageJ and can be downloaded along with a user manual, a troubleshooting section and other information required for its use from http://www.ifc.unam.mx or http://www.ifc.unam.mx/ffm/index.html.

Extraction of liver vessel centerlines under guidance of patient-specific models

Fast extraction of blood vessels of abdominal organs is still a challenging task especially in intra-procedural treatments due to large tissue deformation. In this study, we propose a novel joint vessel extraction and registration framework. This vessel extraction technique is under the guidance of prior knowledge patient specific models. The proposed technique automatically provides correspondence between extracted vessels and pre-procedural vessels, which is important for image guidance such as labeled vessels from pre-procedural models, improves the quality of disease diagnosis using multiple images and follow-up, and provides important information for nonrigid image registration. Another key component in our framework is to dynamically update mapped pre-procedural models by rapidly registering the patient model to the current image based on strain energy, point marks and 3D extracted vessels currently available. We have demonstrated the effectiveness of our technique in extraction of vessels from liver MR images. Validation shows a extraction error of 3.99 mm. This technique has the potential to significantly improve the quality of intra-procedural image guidance, diagnosis of disease and treatment planning.

A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image

The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.

Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients

Background

Hypertension may increase tortuosity or twistedness of arteries. We applied a centerline extraction algorithm and tortuosity metric to magnetic resonance angiography (MRA) brain images to quantitatively measure the tortuosity of arterial vessel centerlines. The most commonly used arterial tortuosity measure is the distance factor metric (DFM). This study tested a DFM based measurement’s ability to detect increases in arterial tortuosity of hypertensives using existing images. Existing images presented challenges such as different resolutions which may affect the tortuosity measurement, different depths of the area imaged, and different artifacts of imaging that require filtering.

Methods

The stability and accuracy of alternative centerline algorithms was validated in numerically generated models and test brain MRA data. Existing images were gathered from previous studies and clinical medical systems by manually reading electronic medical records to identify hypertensives and negatives. Images of different resolutions were interpolated to similar resolutions. Arterial tortuosity in MRA images was measured from a DFM curve and tested on numerically generated models as well as MRA images from two hypertensive and three negative control populations. Comparisons were made between different resolutions, different filters, hypertensives versus negatives, and different negative controls.

Results

In tests using numerical models of a simple helix, the measured tortuosity increased as expected with more tightly coiled helices. Interpolation reduced resolution-dependent differences in measured tortuosity. The Korean hypertensive population had significantly higher arterial tortuosity than its corresponding negative control population across multiple arteries. In addition one negative control population of different ethnicity had significantly less arterial tortuosity than the other two.

Conclusions

Tortuosity can be compared between images of different resolutions by interpolating from lower to higher resolutions. Use of a universal negative control was not possible in this study. The method described here detected elevated arterial tortuosity in a hypertensive population compared to the negative control population and can be used to study this relation in other populations.

A Hessian-based filter for vascular segmentation of noisy hepatic CT scans

PURPOSE

Extraction and enhancement of tubular structures are important in image processing applications, especially in the analysis of liver CT scans where delineation of vascular structures is needed for surgical planning. Portal vein cross-sections have circular or elliptical shapes, so an algorithm must accommodate both. A vessel segmentation method based on medial-axis points was developed and tested on portal veins in CT images.

METHODS

A medial-axis enhancement filter was developed. Consider a line passing through a point inside a tube and intersecting the edges of the tube. If the point is located on the medial axis, the distance of the point in the direction of the line to the edges of the tube will be equal. This feature was employed in a multi-scale framework to identify liver vessels. Dynamic thresholding was used to reduce noise sensitivity. The isotropic coefficient introduced by Pock et al. was used to reduce the response of the filter for asymmetric cross-sections.

RESULTS

Quantitative and qualitative evaluation of the proposed method were performed using both 2D/3D and synthetic/clinical datasets. Compared to other methods for medial-axis enhancement, our method produces better results in low-resolution CT images. Detection rate of the medial axis by the proposed method in a noisy image of standard deviation equal to 0.3 is 68% higher than prior methods.

CONCLUSION

A new Hessian-based method for medial axis vessel segmentation was developed and tested. This method produced superior results compared to prior methods. This new method has the potential for many applications of medial-axis enhancement.

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

PURPOSE

The purpose of this paper is to present a methodology to estimate the carotid artery lumen centerlines in ultrasound (US) images obtained in a free-hand examination. Challenging aspects here are speckle noise in US images, artifacts, and the lack of contrast in the direction orthogonal to the US beam direction.

METHOD

An algorithm based on a rough lumen segmentation obtained by robust ellipse fitting was developed to deal with these conditions and estimate the lumen center in 2D B-mode scans. In a free-hand sweep examination, continuous image acquisitions are performed through time when the radiologist moves the probe on the patient's neck. The result is a series of images that show 2D cross-sections of the carotid's morphology. A tracking sensor (Flock of Birds) was attached to the probe and both were connected to a PC executing the Stradwin software, which relates spatial information to the acquisition data of the US probe. The spatial information was combined with the 2D lumen center estimates to provide a centerline in 3D. For validation, 19 carotid scans from 15 different patients were scanned, their centerlines calculated by the algorithm and compared with results acquired by manual annotations.

RESULTS

The average Euclidean distance between both among all the examinations was 0.82  mm. For each examination, the percentage of these Euclidean distances below 2  mm was calculated; the average over all examinations was 92%.

CONCLUSION

Automated 3D estimation of carotid artery lumen centerlines in free-hand real-time ultrasound is feasible and can be performed with high accuracy. The algorithm is robust enough to keep the centerlines inside the vessel, even in the absence of contrast in parts of the vessel wall.

Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method

RATIONAL AND OBJECTIVES

To evaluate our prototype method for segmentation and tracking of the coronary arterial tree, which is the foundation for a computer-aided detection (CADe) system to be developed to assist radiologists in detecting non-calcified plaques in coronary CT angiography (cCTA) scans.

MATERIALS AND METHODS

The heart region was first extracted by a morphological operation and an adaptive thresholding method based on expectation-maximization (EM) estimation. The vascular structures within the heart region were enhanced and segmented using a multiscale coronary response (MSCAR) method that combined 3D multiscale filtering, analysis of the eigenvalues of Hessian matrices and EM estimation segmentation. After the segmentation of vascular structures, the coronary arteries were tracked by a 3D dynamic balloon tracking (DBT) method. The DBT method started at two manually identified seed points located at the origins of the left and right coronary arteries (LCA and RCA) for extraction of the arterial trees. The coronary arterial trees of a data set containing 20 ECG-gated contrast-enhanced cCTA scans were extracted by our MSCAR-DBT method and a clinical GE Advantage workstation. Two experienced thoracic radiologists visually examined the coronary arteries on the original cCTA scans and the rendered volume of segmented vessels to count the untracked false-negative (FN) segments and false positives (FPs) for both methods.

RESULTS

For the visible coronary arterial segments in the 20 cases, the radiologists identified that 25 segments were missed by our MSCAR-DBT method, ranging from 0 to 5 FN segments in individual cases, and that 55 artery segments were missed by the GE software, ranging from 0 to 7 FN segments in individual cases. 19 and 15 FPs were identified in our and the GE coronary trees, ranging from 0 to 4 FPs for both methods in individual cases, respectively.

CONCLUSION

The preliminary study demonstrates the feasibility of our MSCAR-DBT method for segmentation and tracking coronary artery trees. The results indicated that both our method and GE software can extract coronary artery trees reasonably well and the performance of our method is superior to that of GE software in this small data set. Further studies are underway to develop methods for improvement of the segmentation and tracking accuracy.

Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software

We present on-going work on multi-resolution sulcal-separable meshing for approach-specific neurosurgery simulation, in conjunction multi-grid and Total Lagrangian Explicit Dynamics finite elements. Conflicting requirements of interactive nonlinear finite elements and small structures lead to a multi-grid framework. Implications for meshing are explicit control over resolution, and prior knowledge of the intended neurosurgical approach and intended path. This information is used to define a subvolume of clinical interest, within some distance of the path and the target pathology. Restricted to this subvolume are a tetrahedralization of finer resolution, the representation of critical tissues, and sulcal separability constraint for all mesh levels.

Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images

The blood vessels and nerve trees consist of tubular objects interconnected into a complex tree- or web-like structure that has a range of structural scale 5 μm diameter capillaries to 3 cm aorta. This large-scale range presents two major problems; one is just making the measurements, and the other is the exponential increase of component numbers with decreasing scale. With the remarkable increase in the volume imaged by, and resolution of, modern day 3D imagers, it is almost impossible to make manual tracking of the complex multiscale parameters from those large image data sets. In addition, the manual tracking is quite subjective and unreliable. We propose a solution for automation of an adaptive nonsupervised system for tracking tubular objects based on multiscale framework and use of Hessian-based object shape detector incorporating National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) image processing libraries.

Estimation of local orientations in fibrous structures with applications to the Purkinje system

The extraction of the cardiac Purkinje system (PS) from intensity images is a critical step toward the development of realistic structural models of the heart. Such models are important for uncovering the mechanisms of cardiac disease and improving its treatment and prevention. Unfortunately, the manual extraction of the PS is a challenging and error-prone task due to the presence of image noise and numerous fiber junctions. To deal with these challenges, we propose a framework that estimates local fiber orientations with high accuracy and reconstructs the fibers via tracking. Our key contribution is the development of a descriptor for estimating the orientation distribution function (ODF), a spherical function encoding the local geometry of the fibers at a point of interest. The fiber/branch orientations are identified as the modes of the ODFs via spherical clustering and guide the extraction of the fiber centerlines. Experiments on synthetic data evaluate the sensitivity of our approach to image noise, width of the fiber, and choice of the mode detection strategy, and show its superior performance compared to those of the existing descriptors. Experiments on the free-running PS in an MR image also demonstrate the accuracy of our method in reconstructing such sparse fibrous structures.

Segmentation of nerve bundles and ganglia in spine MRI using particle filters

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

Vascular active contour for vessel tree segmentation

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.

Automatic blood vessel localization in small field of view eye images

Localizing blood vessels in eye images is a crucial step in the automated and objective diagnosis of eye diseases. Most previous research has focused on extracting the centerlines of vessels in large field of view images. However, for diagnosing diseases of the optic disk region, like glaucoma, small field of view images have to be analyzed. One needs to identify not only the centerlines, but also vessel widths, which vary widely in these images. We present an automatic technique for localizing vessels in small field of view images using multi-scale matched filters. We also estimate local vessel properties - width and orientation - along the length of each vessel. Furthermore, we explicitly account for highlights on thick vessels - central reflexes - which are ignored in many previous works. Qualitative and quantitative results demonstrate the efficacy of our method - e.g. vessel centers are localized with RMS and median errors of 2.11 and 1 pixels, respectively in 700×700 images.

Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.

Identification of the Shape of Curvilinear Beams and Fibers

This work concerns the shape identification of curvilinear objects, for example bent beams or wires in mechanics. The beam’s digital picture is analyzed with the introduced Virtual Image Correlation method. This one consists in finding the optimal correlation between the beam’s image and a virtual beam, whose curvature field is described by a truncated series. The gray level and amplitude of the virtual beam does not need to reproduce exactly the ones of the physical beam image. The analytical form of the optimal shape allows one to derive mechanical properties: the identification of the Young’s modulus of a bar is given as an example. We will also show the robustness of the method with regards to the quality of the image.

The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography

Histological and magnetic resonance imaging studies have demonstrated that age-associated alterations of the human brain may be at least partially related to vascular alterations. Relatively little information has been published on vascular changes associated with healthy aging, however. The study presented in this paper examined vessels segmented from standardized, high-resolution, magnetic resonance angiograms (MRAs) of 100 healthy volunteers (50 males, 50 females), aged 18-74, without hypertension or other disease likely to affect the vasculature. The subject sample was divided into 5 age groups (n=20/group) with gender equally distributed per group. The anterior cerebral, both middle cerebral, and the posterior circulations were examined for vessel number, vessel radius, and vessel tortuosity. Males exhibited larger vessel radii regardless of age and across all anatomical regions. Both males and females displayed a lower number of MRA-discernible vessels with age, most marked in the posterior circulation. Age-associated tortuosity increases were relatively mild. Our multi-modal image database has been made publicly available for use by other investigators.

Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach

PURPOSE

The application and large-scale evaluation of minimum cost path approaches for coronary centerline extraction from computed tomography coronary angiography (CTCA) data and the development and evaluation of a novel method to reduce the user-interaction time.

METHODS

A semiautomatic method based on a minimum cost path approach is evaluated for two different cost functions. The first cost function is based on a frequently used vesselness measure and intensity information, and the second is a recently proposed cost function based on region statistics. User interaction is minimized to one or two mouse clicks distally in the coronary artery. The starting point for the minimum cost path search is automatically determined using a newly developed method that finds a point in the center of the aorta in one of the axial slices. This step ensures that all computationally expensive parts of the algorithm can be precomputed.

RESULTS

The performance of the aorta localization procedure was demonstrated by a success rate of 100% in 75 images. The success rate and accuracy of centerline extraction was quantitatively evaluated on 48 coronary arteries in 12 images by comparing extracted centerlines with a manually annotated reference standard. The method was able to extract 88% and 47% of the vessel center-lines correctly using the vesselness/intensity and region statistics cost function, respectively. For only the proximal part of the vessels these values were 97% and 86%, respectively. Accuracy of centerline extraction, defined as the average distance from correctly automatically extracted parts of the centerline to the reference standard, was 0.64 mm for the vesselness/intensity and 0.51 mm for the region statistics cost function. The interobserver variability was 99% for the success rate measure and 0.42 mm for the accuracy measure. Qualitative evaluation using the best performing cost function resulted in successful centerline extraction for 233 out of the 252 coronaries (92%) in 63 additional CTCA images.

CONCLUSIONS

The presented results, in combination with minimal user interaction and low computation time, show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.

The effects of oxygen stresses on the development of features of severe retinopathy of prematurity: knowledge from the 50/10 OIR model

The objective of this study is to determine growth factor expression and activation of signaling pathways associated with intravitreous neovascularization and peripheral avascular retina using a model of retinopathy of prematurity (ROP) relevant to today with oxygen monitoring in neonatal units. Studies using 50/10 oxygen-induced retinopathy (OIR) and 50/10 OIR+SO models were reviewed. Repeated fluctuations in oxygen increased retinal vascular endothelial growth factor (VEGF) even while peripheral avascular retina persisted and prior to the development of intravitreous neovascularization. Repeated fluctuations in oxygen increased VEGF(164) expression but not VEGF(120). Neutralizing VEGF bioactivity significantly reduced intravitreous neovascularization and arteriolar tortuosity without interfering with ongoing retinal vascularization. Repeated oxygen fluctuations led to retinal hypoxia and increased reactive oxygen species (ROS). Inhibiting ROS with NADPH oxidase inhibitor, apocynin, reduced avascular retina by interfering with apoptosis. Supplemental oxygen reduced retinal VEGF concentration and exacerbated NADPH oxidase activation to contribute to intravitreous neovascularization through activation of the JAK/STAT pathway. Oxygen stresses relevant to those experienced by preterm infants today trigger signaling of different pathways to cause avascular retina and intravitreous neovascularization. Increased signaling of VEGF appears important to the development of both avascular retina and intravitreous neovascularization.

Detecting tubular structures via direct vector field singularity characterization

The initial step of vessel segmentation in 3D is the detection of vessel centerlines. The proposed methods in literature are either dependent on vessel radius and/or have low response at vessel bifurcations. In this paper we propose a 3D tubular structure detection method that removes these two drawbacks. The proposed method exploits the observations on the eigenvalues of the Hessian matrix as is done in literature, yet it employs a direct 3D vector field singularity characterization. The Gradient Vector Flow vector field is used and the eigenvalues of its Jacobian are exploited in computing a parameter free vesselness map. Results on phantom and real patient data exhibit robustness to scale, high response at vessel bifurcations, and good noise/non-vessel structure suppression.

Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images

This paper describes an algorithm for extracting pulmonary vascular trees (arteries plus veins) from three-dimensional (3D) thoracic computed tomographic (CT) images. The algorithm integrates tube enhancement filter and traversal approaches which are based on eigenvalues and eigenvectors of a Hessian matrix to extract thin peripheral segments as well as thick vessels close to the lung hilum. The resultant algorithm was applied to a simulation data set and 44 scans from 22 human subjects imaged via multidetector-row CT (MDCT) during breath holds at 85% and 20% of their vital capacity. A quantitative validation was performed with more than 1000 manually identified points selected from inside the vessel segments to assess true positives (TPs) and 1000 points randomly placed outside of the vessels to evaluate false positives (FPs) in each case. On average, for both the high and low volume lung images, 99% of the points was properly marked as vessel and 1% of the points were assessed as FPs. Our hybrid segmentation algorithm provides a highly reliable method of segmenting the combined pulmonary venous and arterial trees which in turn will serve as a critical starting point for further quantitative analysis tasks and aid in our overall goal of establishing a normative atlas of the human lung.

Sampling and visualizing creases with scale-space particles

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n+1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI.

The effect of exercise on the cerebral vasculature of healthy aged subjects as visualized by MR angiography

BACKGROUND AND PURPOSE

Prior studies suggest that aerobic exercise may reduce both the brain atrophy and the decline in fractional anisotropy observed with advancing age. It is reasonable to hypothesize that exercise-induced changes to the vasculature may underlie these anatomic differences. The purpose of this blinded study was to compare high-activity and low-activity healthy elderly volunteers for differences in the cerebrovasculature as calculated from vessels extracted from noninvasive MR angiograms (MRAs).

MATERIALS AND METHODS

Fourteen healthy elderly subjects underwent MRA. Seven subjects reported a high level of aerobic activity (64 +/- 5 years of age; 5 men, 2 women) and 7, a low activity level (68 +/- 6 years of age; 5 women, 2 men). Following vessel segmentation from MRA by an individual blinded to subject activity level, quantitative measures of vessel number, radius, and tortuosity were calculated and histogram analysis of vessel number and radius was performed.

RESULTS

Aerobically active subjects exhibited statistically significant reductions in vessel tortuosity and an increased number of small vessels compared with less active subjects.

CONCLUSIONS

Aerobic activity in elderly subjects is associated with lower vessel tortuosity values and an increase in the number of small-caliber vessels. It is possible that an aerobic exercise program may contribute to healthy brain aging. MRA offers a noninvasive approach to visualizing the cerebral vasculature and may prove useful in future longitudinal investigations.