A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives

This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring

Voxel octree intersection based 3D scanning

Recent developments in the field of three dimensional (3D) printing have resulted in widely available low-cost 3D printers. These printers require 3D models, which are traditionally created in 3D modeling software or are created from 3D scans of existing objects. To be printable, these models must exhibit the property of being watertight. In this thesis, a technique is developed, which, in combination with a custom built low-cost 3D scanner, produces watertight 3D models. Models produced by this technique - the voxel octree intersection technique - do not require any additional processing prior to 3D printing. Results from using this technique with the custom built scanner are examined, and along with the effects of changing various parameters to the technique

Model generation from multiple volumes using constrained elastic SurfaceNets

Three dimensional models of anatomical structures are currently used to aid in medical diagnosis, treatment, surgical guidance, and surgical simulation. Limitations on the resolution of medical scans can cause artifacts to appear in the models that do not exist in the patient’s anatomy. The most severe artifacts occur due to the low sampling rate between image slices of a scan. This paper describes a method of combining two orthogonal scans to generate a model with higher resolution than models created from either of the scans alone. The two scans are first registered to each other and then a net of linked surface nodes is initialized for each of the scans. The nodes from the two nets are then merged and relaxed, subject to constraints set by the resolution of each scan. This generates a smooth surface representation which stays faithful to the original binary data.

Statistical models in medical image analysis

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 149-156).Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and segmentation, which localizes the anatomical structures in an image. The noise and artifacts present in the scans, combined with the complexity and variability of patient anatomy, limit the effectiveness of simple image processing routines. Statistical models provide application-specific context to the problem by incorporating information derived from a training set consisting of instances of the problem along with the solution. In this thesis, we explore the benefits of statistical models for medical image registration and segmentation. We present a technique for computing the rigid registration of pairs of medical images of the same patient. The method models the expected joint intensity distribution of two images when correctly aligned. The registration of a novel set of images is performed by maximizing the log likelihood of the transformation, given the joint intensity model. Results aligning SPGR and dual-echo magnetic resonance scans demonstrate sub-voxel accuracy and large region of convergence. A novel segmentation method is presented that incorporates prior statistical models of intensity, local curvature, and global shape to direct the segmentation toward a likely outcome. Existing segmentation algorithms generally fit into one of the following three categories: boundary localization, voxel classification, and atlas matching, each with different strengths and weaknesses. Our algorithm unifies these approaches. A higher dimensional surface is evolved based on local and global priors such that the zero level set converges on the object boundary. Results segmenting images of the corpus callosum, knee, and spine illustrate the strength and diversity of this approach.by Michael Emmanuel Leventon.Ph.D