Binary morphological shape-based interpolation applied to 3-D tooth reconstruction

Visualization of plaque distribution in a curved artery: three-dimensional intravascular ultrasound imaging

Intravascular ultrasound (IVUS) imaging provides an excellent tool for evaluation of the type, morphology, extent, and severity of an atheromatous plaque. 3 D IVUS imaging offers additive information pertaining to morphology of the arterial structures and volumetric plaque distributions. A new 3 D IVUS visualization technique was developed to provide 3 D structural information of a curved artery. A virtual 3 D curved arterial phantom consisting of varying cross-sectional shapes, wall thicknesses, and acoustic intensity information was utilized to validate the nonlinear interpolation technique to create intermediary 2 D IVUS images. IVUS imaging was performed for the iliofemoral arterial segment of an atherosclerotic Yucatan miniswine model. These in-vivo IVUS data were utilized for intermediary IVUS image generation and volumetric 3 D IVUS visualization. Smooth transitional changes of cross-sectional shape, wall thickness and grayscale intensity were found between the intermediary images and the original arterial phantom slices. The 3 D IVUS imaging of the unfolded curved iliofemoral artery provided realistic 3 D luminal surface images of the arteries with physiologic grayscale intensity information. This unique 3 D IVUS imaging technique may help with assessment of 3 D plaque distribution across the curved arterial structure, and improve 3 D visualization of atheromatous components.

A framework for quantification and visualization of segmentation accuracy and variability in 3D lateral ventricle ultrasound images of preterm neonates

PURPOSE

Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates. Three dimensional ultrasound (US) imaging systems have been developed to visualize 3D anatomical structure of preterm neonatal intracranial ventricular system with IVH and ventricular dilation. To allow quantitative analysis, the ventricle system is required to be segmented accurately and efficiently from 3D US images. Although semiautomatic segmentation algorithms have been developed, local segmentation accuracy and variability associated with these algorithms should be evaluated statistically before they can be applied in clinical settings. This work proposes a statistical framework to quantify the local accuracy and variability and performs statistical tests to identify locations where the semiautomatically segmented surfaces are significantly different from manually segmented surfaces.

METHODS

Three dimensional lateral ventricle US images of preterm neonates were each segmented six times manually and using a semiautomated segmentation algorithm. The local difference between manually and algorithmically segmented surfaces as well as the segmentation variability for each method was computed and superimposed on the ventricular surface of each subject. To summarize the segmentation performance for a whole group of subjects, the subject-specific local difference and standard deviation maps were registered onto a 3D template ventricular surface using a nonrigid registration algorithm. Pointwise, intersubject average accuracy and pooled variability for the whole group of subjects can be computed and visualized on the template surface, providing a summary of performance of the segmentation algorithm for the whole group of ventricles with highly variable geometry. In addition to pointwise statistical analysis performed on the template surface, statistical conclusion regarding the accuracy of the segmentation algorithm was made for subregions and the whole ventricle with the spatial correlation of pointwise accuracy taken into account.

RESULTS

Ten 3D US images were involved in this study. Pointwise local difference, ΔS, its absolute value |ΔS| as well as the standard deviations of the manual and algorithm segmentations were computed and superimposed on the each ventricle surface. Regions with lower segmentation accuracy and higher segmentation variability can be identified from these maps, and the localized information was applied to improve the accuracy of the algorithm. Intersubject average ΔS and |ΔS| as well as pooled standard deviations was computed on the template surface. Intersubject average ΔS and |ΔS| indicated that the algorithm underestimated regions in the neighborhood of the tips of anterior, inferior, and posterior horns. Intersubject pooled standard deviations indicated that manual segmentation had a higher segmentation variability than algorithm segmentation over the whole ventricle. Statistical analysis on the template surface showed that there was significant difference between algorithm and manual methods for segmenting the right lateral ventricle but not for the left lateral ventricle.

CONCLUSIONS

A framework was proposed for evaluating, visualizing, and summarizing the local accuracy and variability of a segmentation algorithm. This framework can be used for improving the accuracy of segmentation algorithms, as well as providing useful feedback to improve the manual segmentation performance. More importantly, this framework can be applied for longitudinal monitoring of local ventricular changes of neonates with IVH.

Tooth shape reconstruction from dental CT images with the region-growing method

OBJECTIVES

The three-dimensional shape information of teeth provides useful information. However, obtaining accurate three-dimensional shapes of teeth is difficult without extracting them physically. In this study, we aimed to develop a method for automatically extracting accurate three-dimensional shapes of teeth from dental CT images.

METHODS

The proposed method includes pre-processing and region extraction. Pre-processing is a combination of image-processing techniques that enhances tooth regions. In the region-extraction process, the region-growing method is introduced for extracting a region of each tooth. Constraint conditions determined by considering the characteristics of the structure of teeth are introduced for accurate extraction. Finally, morphological image processing is applied for eliminating discontinuous points.

RESULTS

We carried out an experiment in which the three-dimensional shapes of teeth were reconstructed from dental CT images. Quantitative evaluation was performed by measuring the three-dimensional spatial accordance rates between the region obtained by the proposed method and the manually extracted region. The proposed method was significantly more accurate than an existing method at the 5% level.

CONCLUSIONS

The experimental results showed that the proposed method reconstructs the shapes of teeth with high precision. However, an unextracted region remained at the surface of the enamel. Solving this problem and improving the extraction accuracy are important topics for future work.

Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation

Background

Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation.

Methods

We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared.

Results

In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method.

Conclusions

This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution.

A morphology-based approach for interslice interpolation of anatomical slices from volumetric images

This paper proposes a new morphology-based approach for the interslice interpolation of current transformer (CT) and MRI datasets composed of parallel slices. Our approach is object based and accepts as input data binary slices belonging to the same anatomical structure. Such slices may contain one or more regions, since topological changes between two adjacent slices may occur. Our approach handles explicitly interslice topology changes by decomposing a many-to-many correspondence into three fundamental cases: one-to-one, one-to-many, and zero-to-one correspondences. The proposed interpolation process is iterative. One iteration of this process computes a transition sequence between a pair of corresponding input slices, and selects the element located at equal distance from the input slices. This algorithmic design yields a gradual, smooth change of shape between the input slices. Therefore, the main contribution of our approach is its ability to interpolate between two anatomic shapes by creating a smooth, gradual change of shape, and without generating over-smoothed interpolated shapes.

Inter-subject comparison of MRI knee cartilage thickness

In this paper, we present the development and application of current image processing techniques to perform MRI inter-subject comparison of knee cartilage thickness based on the registration of bone structures. Each point in the bone surface which is part of the bone-cartilage interface is assigned a cartilage thickness value. Cartilage and corresponding bone structures are segmented and their shapes interpolated to create isotropic voxels. Cartilage thicknesses are computed for each point in the bone-cartilage interfaces and transferred to the bone surfaces. Corresponding anatomic points are then computed for bone surfaces based on shape matching using 3D shape descriptors called shape contexts to register bones with affine and elastic transformations, and then perform a point to point comparison of cartilage thickness values. An alternative technique for cartilage shape interpolation using a morphing technique is also presented. The cartilage segmentation and morphing were validated visually, based on volumetric measurements of porcine knee images which cartilage volumes were measured using a water displacement method, and based on digital thickness values computed with an established technique. Shape matching using 3D shape contexts was validated visually and against manual shape matching performed by a radiologist. The reproducibility of intra- and inter-subject cartilage thickness comparisons was established, as well as the feasibility of using the proposed technique to build a mean femoral shape, cartilage thickness map, and cartilage coverage map. Results showed that the proposed technique is robust, accurate, and reproducible to perform point to point inter-subject comparison of knee cartilage thickness values.

Dynamic visualization of the developing nervous system of the bullfrog, Rana catesbeiana

Anuran amphibians undergo a rapid and dramatic process of metamorphosis featuring widespread structural reorganization of the central nervous system. Although morphological changes during embryonic stages of anuran development have been well documented, much less information is available describing structural changes in the brain during larval (tadpole) stages. Using still images from cresyl-violet-stained material, we present an adaptation of the digital image and video manipulation technique of morphing that allows these images to be compiled in such a manner as to highlight key periods in tadpole brain development in a dynamic fashion. We present three morphed video data sets from ranid tadpoles that facilitate the identification of developmental changes in nuclear boundaries at different levels of the neuraxis. The use of animation allows dynamic examination of anatomical changes across long developmental spans without requiring additional anatomical preparations or specialized expensive equipment.

Application of an adaptive control grid interpolation technique to morphological vascular reconstruction

The problem of interslice magnetic resonance (MR) image reconstruction arises in a broad range of medical applications. In such cases, there is a need to approximate information present in the original subject that is not reflected in contiguously acquired MR images because of hardware sampling limitations. In the context of vascular morphology reconstruction, this information is required in order for subsequent visualization and computational analysis of blood vessels to be most effective. Toward that end we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation (ACGI) to function as a precursor to visualization and computational analysis. ACGI has previously been implemented in addressing various problems including video coding and tracking. This paper focuses on the novel application of the technique to medical image processing. ACGI combines features of optical flow-based and block-based motion estimation algorithms to enhance insufficiently dense MR data sets accurately with a minimal degree of computational complexity. The resulting enhanced data sets describe vascular geometries. These reconstructions can then be used as visualization tools and in conjunction with computational fluid dynamics (CFD) simulations to offer the pressure and velocity information necessary to quantify power loss. The proposed ACGI methodology is envisioned ultimately to play a role in surgical planning aimed at producing optimal vascular configurations for successful surgical outcomes.