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

A Computational geometry approach to automated pulmonary fissure segmentation in CT examinations

Identification of pulmonary fissures, which form the boundaries between the lobes in the lungs, may be useful during clinical interpretation of computed tomography (CT) examinations to assess the early presence and characterization of manifestation of several lung diseases. Motivated by the unique nature of the surface shape of pulmonary fissures in 3-D space, we developed a new automated scheme using computational geometry methods to detect and segment fissures depicted on CT images. After a geometric modeling of the lung volume using the marching cubes algorithm, Laplacian smoothing is applied iteratively to enhance pulmonary fissures by depressing nonfissure structures while smoothing the surfaces of lung fissures. Next, an extended Gaussian image based procedure is used to locate the fissures in a statistical manner that approximates the fissures using a set of plane "patches." This approach has several advantages such as independence of anatomic knowledge of the lung structure except the surface shape of fissures, limited sensitivity to other lung structures, and ease of implementation. The scheme performance was evaluated by two experienced thoracic radiologists using a set of 100 images (slices) randomly selected from 10 screening CT examinations. In this preliminary evaluation 98.7% and 94.9% of scheme segmented fissure voxels are within 2 mm of the fissures marked independently by two radiologists in the testing image dataset. Using the scheme detected fissures as reference, 89.4% and 90.1% of manually marked fissure points have distance </= 2 mm to the reference suggesting a possible under-segmentation of the scheme. The case-based root mean square (rms) distances ("errors") between our scheme and the radiologist ranged from 1.48 +/-0.92 to 2.04 +/-3.88 mm. The discrepancy of fissure detection results between the automated scheme and either radiologist is smaller in this dataset than the interreader variability.

Vessel target location estimation during the TIPS procedure

Creation of a transjugular intrahepatic portosystemic shunt (TIPS) requires passage of a needle toward a moving target that is only seen transiently by X-ray prior to needle passage. Intraoperative, 3D target localization would facilitate target access and improve the safety of the procedure. The clinical assumption is that patients undergoing the TIPS procedure possess rigid, cirrhotic livers that undergo only intraoperative translation without significant deformation or rotation. Based upon this assumption, we hypothesize that the position of any unseen, 3D target point within the liver can be determined intraoperatively by precalculation of the relative positions of the target point to a different 3D point that can be tracked intraoperatively. This paper examines this hypothesis using intraoperatively acquired, biplane, X-ray images of seven patients. In six, we tracked the effects of cardiac and respiratory motion, and in three the effects of needle pressure. Methods involved reconstruction of 3D vessel bifurcation and other trackable intrahepatic points from biplane angiograms, measurement of liver deformation by examining changing distances between these 3D points over time, and comparison of expected to actual displacements of these points with respect to a fixed reference point in the liver. We conclude that, for the rigid livers associated with patients undergoing TIPS, that there is less intraoperative deformation than previously reported by other groups addressing healthy liver deformation, and that the location of an unseen target can be predicted within 3mm accuracy.

A model-based consecutive scanline tracking method for extracting vascular networks from 2-D digital subtraction angiograms

We propose a new model-based algorithm for the automated tracking of vascular networks in 2-D digital subtraction angiograms. Consecutive scanline profiles are fitted by a parametric imaging model to estimate local vessel center point, radius, edge locations and direction. An adaptive tracking strategy is applied with appropriate termination criteria to track each vessel segment. When tracking stops, to prevent premature termination and to detect bifurcations, a look ahead detection scheme is used to search for possible continuation points of the same vessel segment or those of its bifurcated segments. The proposed algorithm can automatically extract the majority of the vascular network without human interaction other than initializing the start point and direction. Compared to other tracking methods, the proposed method highlights accurate estimation of local vessel geometry. Accurate geometric information and a hierarchical vessel network are obtained which can be used for further quantitative analysis of arterial networks to obtain flow conductance estimates.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions.

On concise 3-D simple point characterizations: a marching cubes paradigm

The centerlines of tubular structures are useful for medical image visualization and computer-aided diagnosis applications. They can be effectively extracted by using a thinning algorithm that erodes an object layer by layer until only a skeleton is left. An object point is "simple" and can be safely deleted only if the resultant image is topologically equivalent to the original. Numerous characterizations of 3-D simple points based on digital topology already exist. However, little work has been done in the context of marching cubes (MC). This paper reviews several concise 3-D simple point characterizations in a MC paradigm. By using the Euler characteristic and a few newly observed properties in the context of connectivity-consistent MC, we present concise and more self-explanatory proofs. We also present an efficient method for computing the Euler characteristic locally for MC surfaces. Performance evaluations on different implementations are conducted on synthetic data and multidetector computed tomography examination of virtual colonoscopy and angiography.

Computerized assessment of vessel morphological changes during treatment of glioblastoma multiforme: report of a case imaged serially by MRA over four years

A patient with glioblastoma multiforme underwent serial computerized analysis of tumor-associated vasculature defined from magnetic resonance angiographic (MRA) scans obtained over almost a four year period. The clinical course included tumor resection with subsequent radiation therapy, a long symptom-free interval, emergence of a new malignant focus, resection of that focus, a stroke, and treatment with chemotherapy and anti-angiogenic therapy. Image analysis methods included segmentation of vessels from each MRA and statistical comparison of vessel morphology over 4 regions of interest (the initial tumor site, the second tumor site, a distant control region, and the entire brain) to the same 4 regions of interest in 50 healthy volunteers (26 females and 24 males; mean age 39 years). Results suggested that following completion of focal radiation therapy (RT) vessel shape abnormalities, if elevated at the time of RT completion, may progressively normalize for months in focal regions, that progressively severe vessel shape abnormalities can precede the emergence of a gadolinium enhancing lesion by months, that lesion resection can produce a dramatic but highly transient drop in abnormal vessel tortuosity both focally and globally, and that treatment with anti-angiogenic agents does not necessarily normalize vessel shape. Quantitative measurements of vessel morphology as defined from MRA may provide useful insights into tumor development and response to therapy.

A variational method for geometric regularization of vascular segmentation in medical images

In this paper, a level-set-based geometric regularization method is proposed which has the ability to estimate the local orientation of the evolving front and utilize it as shape induced information for anisotropic propagation. We show that preserving anisotropic fronts can improve elongations of the extracted structures, while minimizing the risk of leakage. To that end, for an evolving front using its shape-offset level-set representation, a novel energy functional is defined. It is shown that constrained optimization of this functional results in an anisotropic expansion flow which is usefull for vessel segmentation. We have validated our method using synthetic data sets, 2-D retinal angiogram images and magnetic resonance angiography volumetric data sets. A comparison has been made with two state-of-the-art vessel segmentation methods. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our regularization method is a promising tool to improve the efficiency of both techniques.

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets.

Curve-skeleton extraction using iterative least squares optimization

A curve skeleton is a compact representation of 3D objects and has numerous applications. It can be used to describe an object's geometry and topology. In this paper, we introduce a novel approach for computing curve skeletons for volumetric representations of the input models. Our algorithm consists of three major steps: 1) using iterative least squares optimization to shrink models and, at the same time, preserving their geometries and topologies, 2) extracting curve skeletons through the thinning algorithm, and 3) pruning unnecessary branches based on shrinking ratios. The proposed method is less sensitive to noise on the surface of models and can generate smoother skeletons. In addition, our shrinking algorithm requires little computation, since the optimization system can be factorized and stored in the pre-computational step. We demonstrate several extracted skeletons that help evaluate our algorithm. We also experimentally compare the proposed method with other well-known methods. Experimental results show advantages when using our method over other techniques.

On geometric modeling of the human intracranial venous system

We describe a process aiming to construct a 3-D geometric model of the human normal intracranial venous system from MRA data. An analysis of geometric properties of the intracranial veins and sinuses results in proposing three models: circular, elliptic, and free-shape. We formulate a rule based on which a suitable geometric venous model can be selected. The cross-sectional shape of different parts of dural venous sinuses is found to be better approximated by ellipses and free shapes, while for veins the circular and elliptic models are comparable. An analysis of using splines for radii smoothing is also provided. The approach is useful for building venous models in education and clinical applications.

Neutralizing VEGF decreases tortuosity and alters endothelial cell division orientation in arterioles and veins in a rat model of ROP: relevance to plus disease

PURPOSE

To study the effects of vascular endothelial growth factor (VEGF) on endothelial nitric oxide synthetase (eNOS) and retinal vascular tortuosity and cleavage planes in a rat model of retinopathy of prematurity (ROP).

METHODS

Within 4 hours of birth, pups and mothers were cycled between 50% and 10% oxygen daily. At postnatal day (p)12, pups received either intravitreous anti-rat neutralizing antibody to VEGF or control nonimmune rat IgG in one eye and returned to oxygen cycling until p14 when they were placed in room air (RA) for 4 days (50/10 oxygen-induced retinopathy [50/10 OIR]). Tortuosity indices and endothelial cleavage plane angles relative to the long axes of the major retinal vessels during anaphase were calculated from phosphohistone- and Alexa-isolectin-stained retinal flatmounts. Some retinas were processed for eNOS protein or phosphorylated/total eNOS.

RESULTS

Retinas from 50/10 OIR had increased tortuosity over time with peaks at p12 and p14 (P < 0.001 vs. RA) before the development of intravitreous neovascularization, which peaked at p18. Compared with RA, eNOS/actin in 50/10 OIR retinas was increased at p12 (P = 0.0003) and p14 (P = 0.047). Inhibition of VEGF with a neutralizing antibody decreased tortuosity and caused endothelial mitosis cleavage planes to orient in favor of vessel elongation but did not affect eNOS protein or activation.

CONCLUSIONS

In the 50/10 OIR model, a model with relevance to ROP, arteriolar tortuosity, and venous dilation are increased through VEGF, which influences the orientation of endothelial cell cleavage in major arterioles and veins, independent of eNOS.

Clinical validation of three-dimensional tortuosity metrics based on the minimum curvature of approximating polynomial splines

The clinical recognition of abnormal vascular tortuosity is important in the diagnosis of many diseases. Metrics based on three-dimensional (3D) curvature, using approximating polynomial spline-fitting to "data balls" centered along the mid-line of the vessel, minimize digitization errors and give tortuosity values largely independent of the resolution of the imaging system. We applied two of these metrics to a number of clinical vascular systems, using both 2D and 3D datasets. Using abdominal aortograms of low tortuosity, we established their validity by their strong correlation with the ranking of an expert panel of three vascular surgeons. The values of the Spearman rank correlation coefficient between our rankings, using a data ball radius of one-quarter of the local vessel radius, and the average ranking of the expert panel were 0.96 (with a 95% confidence interval of [0.91, 0.99]) for the mean curvature and 0.98 ([0.94, 0.99]) for the root-mean-square (RMS) curvature. These confidence intervals indicate that our automated analysis is producing rankings whose reliability is similar to that of a human expert, and is significantly better than that achieved with existing algorithms. The metrics provided good discrimination between vessels of different tortuosity for both 2D and 3D datasets, and produced values sufficiently discriminating to assess the relative utility of arteries for endoluminal repair of aneurysms.

3D-3D tubular organs registration based on bifurcations for the CT images

The registration of tubular organs (pulmonary tracheobronchial tree or vasculature) of 3D medical images is critical in various clinical applications such as surgical planning and radiotherapy. In this paper, we present a novel method for tubular organs registration based on the automatically detected bifurcation points of the tubular organs. We first perform a 3D tubular organ segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT (RCCT) images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. Our method has over 96% success rate for detecting bifurcation points.We present very promising results of our method applied to the registration of the planning and respiration-correlated CT images. On average, the mean distance and the root-mean-square error (RMSE) of the corresponding bifurcation points between the respiration-correlated images and the registered planning images are less than 2.7 mm.

Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths

We propose a novel framework to segment vessels on their cross-sections. It starts with a probabilistic vessel axis tracing in a gray-scale three-dimensional angiogram, followed by vessel boundary delineation on cross-sections derived from the extracted axis. It promotes a more intuitive delineation of vessel boundaries which are mostly round on the cross-sections. The prior probability density function of the axis tracer's formulation permits seamless integration of user guidance to produce continuous traces through regions that contain furcations, diseased portions, kissing vessels (vessels in close proximity to each other) and thin vessels. The contour that outlines the vessel boundary in a 3-D space is determined as the minimum cost path on a weighted directed acyclic graph derived from each cross-section. The user can place anchor points to force the contour to pass through. The contours obtained are tiled to approximate the vessel boundary surface. Since we use stream surfaces generated w.r.t. the traced axis as cross-sections, non-intersecting adjacent cross-sections are guaranteed. Therefore, the tiling can be achieved by joining vertices of adjacent contours. The vessel boundary surface is then deformed under constrained movements on the cross-sections and is voxelized to produce the final vascular segmentation. Experimental results on synthetic and clinical data have shown that the vessel axes extracted by our tracer are continuous and less jittered as compared with the other two trace-based algorithms. Furthermore, the segmentation algorithm with cross-sections are robust to noise and can delineate vessel boundaries that have level of variability similar to those obtained manually.

Blood vessel morphologic changes depicted with MR angiography during treatment of brain metastases: a feasibility study

PURPOSE

To prospectively determine if magnetic resonance (MR) angiography can depict intracranial vascular morphologic changes during treatment of brain metastases from breast cancer and if serial quantitative vessel tortuosity measurements can be used to predict tumor treatment response sooner than traditional methods.

MATERIALS AND METHODS

Institutional review board approval and informed consent were obtained for this HIPAA-compliant study. Twenty-two women aged 31-61 years underwent brain MR angiography prior to and 2 months after initiation of lapatinib therapy for brain metastases from breast cancer. Vessels were extracted from MR angiograms with a computer program. Changes in vessel number, radius, and tortuosity were calculated mathematically, normalized with values obtained in 34 healthy control subjects (19 women, 15 men; age range, 19-72 years), and compared with subsequent assessments of tumor volume and clinical course.

RESULTS

All patients exhibited abnormal vessel tortuosity at baseline. Nineteen (86%) patients did not exhibit improvement in vessel tortuosity at 2-month follow-up, and all patients demonstrated tumor growth at 4-month follow-up. Vessel tortuosity measurements enabled us to correctly predict treatment failure 1-2 months earlier than did traditional methods. Three (14%) patients had quantitative improvement in vessel tortuosity at 2-month follow-up, with drop out of small abnormal vessels and straightening of large vessels. Each of the two patients for whom further follow-up data were available responded to treatment for more than 6 months.

CONCLUSION

Study results established the feasibility of using MR angiography to quantify vessel shape changes during therapy. Although further research is required, results suggest that changes in vessel tortuosity might enable early prediction of tumor treatment response.

Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a "connected component" analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries and veins, the segmentation accuracy are 97.0% (4546/4689) and 93.8% (4439/4732) for the cases without and with other lung diseases, respectively. Because of the lack of ground truth for the vessels, in addition to quantitative evaluation of the vessel segmentation performance, visual inspection was conducted to evaluate the segmentation. The results demonstrate that vessel segmentation using our method can extract the pulmonary vessels accurately and is not degraded by PE occlusion to the vessels in these test cases.

An improved algorithm for vessel centerline tracking in coronary angiograms

For automated visualization and quantification of artery diseases, the accurate determination of the arterial centerline is a prerequisite. Existing tracking-based approaches usually suffer from the inaccuracy, inflexion and discontinuity in the extracted centerlines, and they may even fail in complicated situations. In this paper, an improved algorithm for coronary arterial centerline extraction is proposed, which incorporates a new tracking direction updating scheme, a self-adaptive magnitude of linear extrapolation and a dynamic-size search window for matched filtering. A simulation study is conducted for the determination of the optimal weighting factor which is used to combine the geometrical topology information and intensity distribution information to obtain the proposed tracking direction. Synthetic and clinical examples, representing some difficult situations that may occur in coronary angiograms, are presented. Results show that the proposed algorithm outperforms the conventional methods. By adopting the proposed algorithm, centerlines are successfully extracted under these complicated situations, and with satisfactory accuracy.

Computer-assisted measurement of vessel shape from 3T magnetic resonance angiography of mouse brain

Blood vessel morphology (vessel radius, branching pattern, and tortuosity) is altered by a multitude of diseases. Although murine models of human pathology are important to the investigation of many diseases, there are few publications that address quantitative measurements of murine vascular morphology. This report outlines methods of imaging mice in vivo using magnetic resonance angiograms obtained on a clinical 3T unit, of defining mouse vasculature from these images, and of quantifying measures of vessel shape. We provide examples of both healthy and diseased vasculature and illustrate how the approach can be used to assess pathology both visually and quantitatively. The method is amenable to the assessment of many diseases in both human beings and mice.

3D planar reformation of vascular central axis surface with biconvex slab

Curved multi-planar reformation (curved MPR) is one of the commonly used vascular visualization methods in clinics. It re-samples and visualizes the vascular central axis surface (VCAS), which is a curved surface passing through the vascular central axis (VCA) or vessel centerline. The rotation of the VCAS along the VCA generates a set of 2D images. In this paper, we introduce a 3D curved MPR method, VCAS planar reformation (VPR) by a convex hull, called a biconvex slab. The entire vessel is enclosed within a biconvex slab and rendered in one image by volume rendering, such as MIP or X-ray. The method is applied to computed tomographic angiography (CTA) data sets. The resulting image is clear and free from obstruction by bones and other adjacent organs.

Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines

In this paper, we propose an innovative approach to the segmentation of tubular structures. This approach combines all of the benefits of minimal path techniques such as global minimizers, fast computation, and powerful incorporation of user input, while also having the capability to represent and detect vessel surfaces directly which so far has been a feature restricted to active contour and surface techniques. The key is to represent the trajectory of a tubular structure not as a 3-D curve but to go up a dimension and represent the entire structure as a 4-D curve. Then we are able to fully exploit minimal path techniques to obtain global minimizing trajectories between two user supplied endpoints in order to reconstruct tubular structures from noisy or low contrast 3-D data without the sensitivity to local minima inherent in most active surface techniques. In contrast to standard purely spatial 3-D minimal path techniques, however, we are able to represent a full tubular surface rather than just a curve which runs through its interior. Our representation also yields a natural notion of a tube's "central curve." We demonstrate and validate the utility of this approach on magnetic resonance (MR) angiography and computed tomography (CT) images of coronary arteries.

Bayesian tracking of elongated structures in 3D images

Tracking of tubular elongated structures is an important goal in a wide range of biomedical imaging applications. A Bayesian tube tracking algorithm is presented that allows to easily incorporate a priori knowledge. Because probabilistic tube tracking algorithms are computationally complex, steps towards a computational efficient implementation are suggested in this paper. The algorithm is evaluated on 2D and 3D synthetic data with different noise levels and clinical CTA data. The approach shows good performance on data with high levels of Gaussian noise.

Automatic segmentation of 3D micro-CT coronary vascular images

Although there are many algorithms available in the literature aimed at segmentation and model reconstruction of 3D angiographic images, many are focused on characterizing only a part of the vascular network. This study is motivated by the recent emerging prospects of whole-organ simulations in coronary hemodynamics, autoregulation and tissue oxygen delivery for which anatomically accurate vascular meshes of extended scale are highly desirable. The key requirements of a reconstruction technique for this purpose are automation of processing and sub-voxel accuracy. We have designed a vascular reconstruction algorithm which satisfies these two criteria. It combines automatic seeding and tracking of vessels with radius detection based on active contours. The method was first examined through a series of tests on synthetic data, for accuracy in reproduced topology and morphology of the network and was shown to exhibit errors of less than 0.5 voxel for centerline and radius detections, and 3 degrees for initial seed directions. The algorithm was then applied on real-world data of full rat coronary structure acquired using a micro-CT scanner at 20 microm voxel size. For this, a further validation of radius quantification was carried out against a partially rescanned portion of the network at 8 microm voxel size, which estimated less than 10% radius error in vessels larger than 2 voxels in radius.

Characteristic quantities of microvascular structures in CLSM volume datasets

A method for fully automated morphological and topological quantification of microvascular structures in confocal laser scanning microscopy (CLSM) volume datasets is presented. Several characteristic morphological and topological quantities are calculated in a series of image-processing steps and can be used to compare single components as well as whole networks of microvascular structures to each other. The effect of the individual image-processing steps is illustrated and characteristic quantities of measured volume datasets are presented and discussed.

Automatic correction of level set based subvoxel precise centerlines for virtual colonoscopy using the colon outer wall

Virtual colonoscopy (VC) is becoming a more prevalent method to detect and diagnose colorectal cancer. An essential component of using VC to detect cancerous polyps, especially in conjunction with computer-aided diagnosis, is the accurate calculation of the centerline of the colon. While the colon is often modeled as a simple cylinder, the amount of colonic distention may vary between patients and within the same patient often causing loops and multiple disconnected segments to be present in the colon segmentation. These variations have caused previous centerline algorithms to fail to capture a complete and accurate centerline for all colons. We have developed an automatic method to determine from a computed tomography (CT) VC a subvoxel precise centerline that is accurate even in cases of over-distended or under-distended colons. In this algorithm, the loops in the colon caused by over-distention are detected and removed when the centerline calculation is performed. Also, a newly developed method for the detection and segmentation of the outer wall of the colon is used to connect collapsed portions of the colon where the lumen segmentation fails to produce a continuous centerline. These two methods allow for a complete and accurate centerline to be calculated in uniformly distended colons as well as in colons containing segments which are over-distended and/or under-distended. We have demonstrated successfully the effectiveness of our algorithm on 50 cases, 25 of which resulted in erroneous solutions by previous centerline algorithms due to variability in the colon distention.

A pilot study using "ROPtool" to quantify plus disease in retinopathy of prematurity

PURPOSE

The accurate diagnosis of plus disease is critical to optimize the timing of laser treatment. Unfortunately, it is highly subjective and error-prone. "ROPtool" is a computer program that automatically traces retinal blood vessels and measures their tortuosity and dilation. Our aims were to pilot ROPtool, determine its reliability and validity, and establish appropriate numerical thresholds for plus and pre-plus disease.

METHODS

Twenty high-quality images of the posterior poles of premature infants were collected. Two of the authors (DKW and SFF) independently judged tortuosity and dilation separately as plus, pre-plus, or normal for each quadrant of each image. Disagreements were adjudicated, and the results were considered to be the standard for comparison to ROPtool. These two authors then separately used ROPtool to analyze the same 20 images.

RESULTS

For determination of tortuosity sufficient for plus disease, ROPtool interuser agreement was 95% (19/20), compared with 90% (18/20) agreement by investigator judgment. Eye-level (2 MDs x 20 eyes) sensitivity of ROPtool in detecting tortuosity sufficient for plus disease averaged 95% (21/22) and specificity averaged 78% (14/18). Quadrant-level (2 MDs x 20 eyes x 4 quadrants) sensitivity averaged 85% (66/78) and specificity averaged 77% (63/82). A numeric threshold for pre-plus disease equal to 70% of the average tortuosity of the standard photograph of plus disease resulted in mean sensitivity of 89% (103/116) and mean specificity of 82% (36/44) in distinguishing quadrant-level tortuosity sufficient for pre-plus disease or worse from normal.

CONCLUSIONS

ROPtool can reduce subjectivity and thereby enhance the evaluation of plus and pre-plus disease.

Integrated Analysis of Vascular and Nonvascular Changes From Color Retinal Fundus Image Sequences

Algorithms are presented for integrated analysis of both vascular and nonvascular changes observed in longitudinal time-series of color retinal fundus images, extending our prior work. A Bayesian model selection algorithm that combines color change information, and image understanding systems outputs in a novel manner is used to analyze vascular changes such as increase/decrease in width, and disappearance/appearance of vessels, as well as nonvascular changes such as appearance/disappearance of different kinds of lesions. The overall system is robust to false changes due to inter-image and intra-image nonuniform illumination, imaging artifacts such as dust particles in the optical path, alignment errors and outliers in the training-data. An expert observer validated the algorithms on 54 regions selected from 34 image pairs. The regions were selected such that they represented diverse types of vascular changes of interest, as well as no-change regions. The algorithm achieved a sensitivity of 82% and a 9% false positive rate for vascular changes. For the nonvascular changes, 97% sensitivity and a 10% false positive rate are achieved. The combined system is intended for diverse applications including computer-assisted retinal screening, image-reading centers, quantitative monitoring of disease onset and progression, assessment of treatment efficacy, and scoring clinical trials.

Segmentation and quantification of human vessels using a 3-D cylindrical intensity model

We introduce a new approach for 3-D segmentation and quantification of vessels. The approach is based on a 3-D cylindrical parametric intensity model, which is directly fitted to the image intensities through an incremental process based on a Kalman filter. Segmentation results are the vessel centerline and shape, i.e., we estimate the local vessel radius, the 3-D position and 3-D orientation, the contrast, as well as the fitting error. We carried out an extensive validation using 3-D synthetic images and also compared the new approach with an approach based on a Gaussian model. In addition, the new model has been successfully applied to segment vessels from 3-D MRA and computed tomography angiography image data. In particular, we compared our approach with an approach based on the randomized Hough transform. Moreover, a validation of the segmentation results based on ground truth provided by a radiologist confirms the accuracy of the new approach. Our experiments show that the new model yields superior results in estimating the vessel radius compared to previous approaches based on a Gaussian model as well as the Hough transform.

Automatic Identification of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features

This paper presents an automated method to identify arteries and veins in dual-wavelength retinal fundus images recorded at 570 and 600 nm. Dual-wavelength imaging provides both structural and functional features that can be exploited for identification. The processing begins with automated tracing of the vessels from the 570-nm image. The 600-nm image is registered to this image, and structural and functional features are computed for each vessel segment. We use the relative strength of the vessel central reflex as the structural feature. The central reflex phenomenon, caused by light reflection from vessel surfaces that are parallel to the incident light, is especially pronounced at longer wavelengths for arteries compared to veins. We use a dual-Gaussian to model the cross-sectional intensity profile of vessels. The model parameters are estimated using a robust M-estimator, and the relative strength of the central reflex is computed from these parameters. The functional feature exploits the fact that arterial blood is more oxygenated relative to that in veins. This motivates use of the ratio of the vessel optical densities (ODs) from images at oxygen-sensitive and oxygen-insensitive wavelengths (ODR = OD600/OD570) as a functional indicator. Finally, the structural and functional features are combined in a classifier to identify the type of the vessel. We experimented with four different classifiers and the best result was given by a support vector machine (SVM) classifier. With the SVM classifier, the proposed algorithm achieved true positive rates of 97% for the arteries and 90% for the veins, when applied to a set of 251 vessel segments obtained from 25 dual wavelength images. The ability to identify the vessel type is useful in applications such as automated retinal vessel oximetry and automated analysis of vascular changes without manual intervention.

Curve-skeleton properties, applications, and algorithms

Curve-skeletons are thinned 1D representations of 3D objects useful for many visualization tasks including virtual navigation, reduced-model formulation, visualization improvement, animation, etc. There are many algorithms in the literature describing extraction methodologies for different applications; however, it is unclear how general and robust they are. In this paper, we provide an overview of many curve-skeleton applications and compile a set of desired properties of such representations. We also give a taxonomy of methods and analyze the advantages and drawbacks of each class of algorithms.

Tumor therapeutic response and vessel tortuosity: preliminary report in metastatic breast cancer

No current non-invasive method is capable of assessing the efficacy of brain tumor therapy early during treatment. We outline an approach that evaluates tumor activity via statistical analysis of vessel shape using vessels segmented from MRA. This report is the first to describe the changes in vessel shape that occur during treatment of metastatic brain tumors as assessed by sequential MRA. In this preliminary study of 16 patients undergoing treatment for metastatic breast cancer we conclude that vessel shape may predict tumor response several months in advance of traditional methods.

A new adaptive probabilistic model of blood vessels for segmenting MRA images

A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with synthetic and 50 real data sets confirm the high accuracy of the proposed approach.

3D/2D model-to-image registration applied to TIPS surgery

We have developed a novel model-to-image registration technique which aligns a 3-dimensional model of vasculature with two semiorthogonal fluoroscopic projections. Our vascular registration method is used to intra-operatively initialize the alignment of a catheter and a preoperative vascular model in the context of image-guided TIPS (Transjugular, Intrahepatic, Portosystemic Shunt formation) surgery. Registration optimization is driven by the intensity information from the projection pairs at sample points along the centerlines of the model. Our algorithm shows speed, accuracy and consistency given clinical data.

From MIP image to MRA segmentation using fuzzy set theory

The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.

Identification of the main arterial branches by whole-body contrast-enhanced MRA in elderly subjects using limited user interaction and fast marching

PURPOSE

To extract a graph model corresponding to a predefined set of arterial branches from whole-body contrast-enhanced magnetic resonance angiography (CE-MRA) data sets in elderly asymptomatic subjects, a high-incidence group.

MATERIALS AND METHODS

Maximum intensity projections (MIPs) were used as an interface to place landmarks in the three-dimensional (3D) data sets. These landmarks were linked together using fast marching to form a graph model of the arterial tree. Only vessels of interest were identified.

RESULTS

We tested our method on 10 subjects. We were able to build a graph model of the main arterial branches that performed well in the presence of vascular pathologies, such as stenosis and aneurysm. The results were rated by an experienced radiologist, with an overall success rate of 80%.

CONCLUSION

We were able to extract chosen arterial branches in 3D whole-body CE-MRA images with a moderate amount of interaction using a single MIP projection.

Vessel axis tracking using topology constrained surface evolution

An approach to 3-D vessel axis tracking based on surface evolution is presented. The main idea is to guide the evolution of the surface by analyzing its skeleton topology during evolution, and imposing shape constraints on the topology. For example, the intermediate topology can be processed such that it represents a single vessel segment, a bifurcation, or a more complex vascular topology. The evolving surface is then reinitialized with the newly found topology. Reinitialization is a crucial step since it creates probing behavior of the evolving front, encourages the segmentation process to extract the vascular structure of interest and reduces the risk on leaking of the curve into the background. The method was evaluated in two computed tomography angiography applications: 1) extracting the internal carotid arteries including the region in which they traverse through the skull base, which is challenging due to the proximity of bone structures and overlap in intensity values; 2) extracting the carotid bifurcations including many cases in which they are severely stenosed and contain calcifications. The vessel axis was found in 90% (18/20 internal carotids in ten patients) and 70% (14/20 carotid bifurcations in a different set of ten patients) of the cases.

Cortical vascular blood flow pattern by laser speckle imaging

The cor tical vascular blood flow pattern is associated with the functional response in cerebral cortex. The pattern of the vascular blood flow can be used to study the spatiotemporal activities of the somatosensory center and the diagnosis of the focal stroke or ischemia. In this present study, tempor al laser speckle analysis is used to obtain the cortical blood flow information. By centerline modeling method based on ridge tracking, we extracted the cortical vessels. By fusing with the laser speckle contrast results, we obtained the image of the cortical vascular blood flow pattern in which both the vessels and the blood velocity can be visualized.

Subvoxel precise skeletons of volumetric data based on fast marching methods

The accurate calculation of the skeleton of an object is a problem not satisfactorily solved by existing approaches. Most algorithms require a significant amount of user interaction and use a voxel grid to compute discrete and often coarse approximations of this representation of the data. We present a novel, automatic algorithm for computing subvoxel precise skeletons of volumetric data based on subvoxel precise distance fields. Most voxel based centerline and skeleton algorithms start with a binary mask and end with a list of voxels that define the centerline or skeleton. Even though subsequent smoothing may be applied, the results are inherently discrete. Our skeletonization algorithm uses as input a subvoxel precise distance field and employs a number of fast marching method propagations to extract the skeleton at subvoxel precision. We present the skeletons of various three-dimensional (3D) data sets and digital phantom models as validations of our algorithm.

Robust 3-D modeling of vasculature imagery using superellipsoids

This paper presents methods to model complex vasculature in three-dimensional (3-D) images using cylindroidal superellipsoids, along with robust estimation and detection algorithms for automated image analysis. This model offers an explicit, low-order parameterization, enabling joint estimation of boundary, centerlines, and local pose. It provides a geometric framework for directed vessel traversal, and extraction of topological information like branch point locations and connectivity. M-estimators provide robust region-based statistics that are used to drive the superellipsoid toward a vessel boundary. A robust likelihood ratio test is used to differentiate between noise, artifacts, and other complex unmodeled structures, thereby verifying the model estimate. The proposed methodology behaves well across scale-space, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. When evaluated on synthetic imagery mimicking specific structural complexities in tumor microvasculature, it consistently produces ubvoxel accuracy estimates of centerlines and widths in the presence of closely-adjacent vessels, branch points, and noise. An edit-based validation demonstrated a precision level of 96.6% at a recall level of 95.4%. Overall, it is robust enough for large-scale application.

Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data

A system for automatic segmentation and labeling of the complete rib cage in chest CT scans is presented. The method uses a general framework for automatic detection, recognition and segmentation of objects in three-dimensional medical images. The framework consists of five stages: (1) detection of relevant image structures, (2) construction of image primitives, (3) classification of the primitives, (4) grouping and recognition of classified primitives and (5) full segmentation based on the obtained groups. For this application, first 1D ridges are extracted in 3D data. Then, primitives in the form of line elements are constructed from the ridge voxels. Next a classifier is trained to classify the primitives in foreground (ribs) and background. In the grouping stage centerlines are formed from the foreground primitives and rib numbers are assigned to the centerlines. In the final segmentation stage, the centerlines act as initialization for a seeded region growing algorithm. The method is tested on 20 CT-scans. Of the primitives, 97.5% is classified correctly (sensitivity is 96.8%, specificity is 97.8%). After grouping, 98.4% of the ribs are recognized. The final segmentation is qualitatively evaluated and is very accurate for over 80% of all ribs, with slight errors otherwise.