Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

Methods for generating high-resolution structural models from electron microscope tomography data

Reconstructed volumes generated by tilt-image electron-microscope tomography offer the best spatial resolution currently available for studying cell structures in situ. Analysis is often accomplished by creating surface models that delineate grayscale contrast boundaries. Here, we introduce a specialized and convenient sequence of segmentation operations for making such models that greatly improves their reliability and spatial resolution as compared to current approaches, providing a basis for making accurate measurements. To assess the reliability of the surface models, we introduce a spatial uncertainty measurement based on grayscale gradient scale length. The model generation and measurement methods are validated by applying them to synthetic data, and their utility is demonstrated by using them to characterize macromolecular architecture of active zone material at the frog's neuromuscular junction.

Morphology-based cortical thickness estimation

We describe a new approach to estimating the cortical thickness of human brains using magnetic resonance imaging data. Our algorithm is part of a processing chain consisting of a brain segmentation (skull stripping), as well as white and grey matter segmentation procedures. In this paper, only the grey matter segmentation together with the cortical thickness estimation is described. In contrast to many existing methods, our estimation method is voxel-based and does not use any surface meshes. While this fact poses a principal limit on the accuracy that can be achieved by our method, it offers tremendous advantages with respect to practical applicability. In particular, it is applicable to data sets showing severe cortical atrophies that involve areas of high curvature and extremely thin gyral stalks. In contrast to many other methods, it is entirely automatic and very fast with computation times of a few minutes. Our method has been used in two clinical studies involving a total of 27 patients and 23 healthy subjects.

Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography

Investigation of three-dimensional (3-D) geometry and fluid-dynamics in human arteries is an important issue in vascular disease characterization and assessment. Thanks to recent advances in magnetic resonance (MR) and computed tomography (CT), it is now possible to address the problem of patient-specific modeling of blood vessels, in order to take into account interindividual anatomic variability of vasculature. Generation of models suitable for computational fluid dynamics is still commonly performed by semiautomatic procedures, in general based on operator-dependent tasks, which cannot be easily extended to a significant number of clinical cases. In this paper, we overcome these limitations making use of computational geometry techniques. In particular, 3-D modeling was carried out by means of 3-D level sets approach. Model editing was also implemented ensuring harmonic mean curvature vectors distribution on the surface, and model geometric analysis was performed with a novel approach, based on solving Eikonal equation on Voronoi diagram. This approach provides calculation of central paths, maximum inscribed sphere estimation and geometric characterization of the surface. Generation of adaptive-thickness boundary layer finite elements is finally presented. The use of the techniques presented here makes it possible to introduce patient-specific modeling of blood vessels at clinical level.

A review of medical imaging informatics

This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I of this review focused on the physics of magnetic resonance angiography (MRA) and prefiltering techniques applied to MRA. Part II of this review presents the state-of-the-art overview, status, and new achievements in vessel segmentation algorithms from MRA. The first part of this review paper is focused on the nonskeleton or direct-based techniques. Here, we present eight different techniques along with their mathematical foundations, algorithms and their pros and cons. We will also focus on the skeleton or indirect-based techniques. We will discuss three different techniques along with their mathematical foundations, algorithms and their pros and cons. This paper also includes a clinical discussion on skeleton versus nonskeleton-based segmentation techniques. Finally, we shall conclude this paper with the possible challenges, the future, and a brief summary on vascular segmentation techniques.