Interpolation of 3-D binary images based on morphological skeletonization

3D Visualization of Brain Tumors Using MR Images: A Survey

BACKGROUND

Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer.

DISCUSSION

It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling.

CONCLUSION

In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.

A novel semiautomatic segmentation protocol to evaluate guided bone regeneration outcomes: A pilot randomized, controlled clinical trial

OBJECTIVES

The aims of this study were to (a) present a novel morphological contour interpolation (MCI) algorithm based method to evaluate grafted bone alterations following guided bone regeneration (GBR), (b) compare clinical and radiological outcomes of GBR with two different collagen membranes.

MATERIALS AND METHODS

The data were retrieved from an ongoing randomized controlled trial. Patients were randomly allocated into two groups: (a) control group (CG): Bio-Gide (b) test group (TG): bovine dermis-derived collagen membrane. Cone beam computed tomography examinations were performed 1 week (T0) and 6 months after surgery (T1). PES/WES at T1, grafted bone volume and density changes from T0 to T1 were recorded.

RESULTS

Thirty-six patients (16/20 in test/control group, respectively) were enrolled in the present study. Excellent inter-observer reliability (ICC ≥ 0.97) was revealed for repeated measurements using this method. Significant volumetric reduction of grafted bone were found in both groups (test group: from 0.60 to 0.39 cm³ , p < 0.01; control group: from 0.54 to 0.31 cm³ , p < 0.01). Mean bone density (gray-scale values) significantly increased from 305.12 to 456.69 in CG (p < 0.01). In TG, it slightly increased from 304.75 to 393.27 (p = 0.25). The mean PES/WES values were 13.84 (6.62/7.22) and 13.90 (6.70/7.20) for TG and CG, respectively. As for inter-group comparison, no significant differences of grafted bone volume change, density change and PES/WES were found between two groups.

CONCLUSION

Within the limitations of this study, the novel MCI-based method is a reproducible tool to segment and visualize changes of grafted bone in 3D. Furthermore, both collagen membranes could be used as a barrier membrane for GBR in humans.

Some Properties of Interpolations Using Mathematical Morphology

The problem of interpolation of images is defined as - given two images at time t = 0 and t = T, one must find the series of images for the intermediate time. This problem is not well posed, in the sense that without further constraints, there are many possible solutions. The solution is thus usually dictated by the choice of the constraints/assumptions, which in turn relies on the domain of application. In this article we follow the approach of obtaining a solution to the interpolation problem using the operators from Mathematical Morphology (MM). These operators have an advantage of preserving structures since the operators are defined on sets. In this work we explore the solutions obtained using MM, and provide several results along with proofs which corroborates the validity of the assumptions, provide links among existing methods and intuition about them. We also summarize few possible extensions and prospective problems of current interest.

Improved Rubin-Bodner model for the prediction of soft tissue deformations

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve.

Digital Topology and Geometry in Medical Imaging: A Survey

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

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.

Feature-guided shape-based image interpolation

A feature-guided image interpolation scheme is presented. It is an effective and improved, shape-based interpolation method used for interpolating image slices in medical applications. The proposed method integrates feature line-segments to guide the shape-based method for better shape interpolation. An automatic method for finding these line segments is given. The proposed feature-guided shape-based method can manage translation, rotation and scaling situations when the slices have similar shapes. It can also interpolate intermediate shapes when the successive slices do not have similar shapes. This method is experimentally evaluated using artificial and real two-dimensional and three-dimensional data. The proposed method generated satisfactory interpolated results in these experiments. We demonstrate the practicality, effectiveness and reproducibility of the proposed method for interpolating medical images.

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

In this paper, we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the n-dimensional object from a group of (n - 1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for three-dimensional tooth reconstruction.