We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

/pdf/marching-cubes-a-high-resolution-3d-surface-construction-3ealswkmtb.pdf

Marching cubes: A high resolution 3D surface construction algorithm

Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Virtually all large systems which evaluate images require the registration of images, or a closely related operation, as an intermediate step. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis.Over the years, a broad range of techniques has been developed for various types of data and problems. These techniques have been independently studied for several different applications, resulting in a large body of research. This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied. Three major types of variations are distinguished. The first type are the variations due to the differences in acquisition which cause the images to be misaligned. To register images, a spatial transformation is found which will remove these variations. The class of transformations which must be searched to find the optimal transformation is determined by knowledge about the variations of this type. The transformation class in turn influences the general technique that should be taken. The second type of variations are those which are also due to differences in acquisition, but cannot be modeled easily such as lighting and atmospheric conditions. This type usually effects intensity values, but they may also be spatial, such as perspective distortions. The third type of variations are differences in the images that are of interest such as object movements, growths, or other scene changes. Variations of the second and third type are not directly removed by registration, but they make registration more difficult since an exact match is no longer possible. In particular, it is critical that variations of the third type are not removed. Knowledge about the characteristics of each type of variation effect the choice of feature space, similarity measure, search space, and search strategy which will make up the final technique. All registration techniques can be viewed as different combinations of these choices. This framework is useful for understanding the merits and relationships between the wide variety of existing techniques and for assisting in the selection of the most suitable technique for a specific problem.

A survey of image registration techniques

https://www.cs.ucla.edu/~dt/papers/mia96/mia96.pdf

Deformable models in medical image analysis: a survey

羟氨苄青霉素引起大疱性类天疱疮样疹

A technique for rendering images of volumes containing mixtures of materials is presented. The shading model allows both the interior of a material and the boundary between materials to be colored. Image projection is performed by simulating the absorption of light along the ray path to the eye. The algorithms used are designed to avoid artifacts caused by aliasing and quantization and can be efficiently implemented on an image computer. Images from a variety of applications are shown.

Volume rendering

The theory, design, implementation and evaluation of a three-dimensional surface detection algorithm

This straightforward 3-D display algorithm traverses voxels slice by slice to project each voxel on the screen. No surface detection or z-buffer is needed.

Back-to-Front Display of Voxel Based Objects

In many three-dimensional imaging applications the three-dimensional scene is represented by a three-dimensional array of volume elements, or voxels for short. A subset Q of the voxels is specified by some property. The objects in the scene are then defined as subsets of Q formed by voxels which are “connected” in some appropriate sense. It is often of interest to detect and display the surface of an object in the scene, specified say by one of the voxels in it.In this paper, the problem of surface detection is translated into a problem of traversal of a directed graph, G. The nodes of G correspond to faces separating voxels in Q from voxels not in Q. It has been proven that connected subgraphs of G correspond to surfaces of connected components of Q (i.e., of objects in the scene). Further properties of the directed graph have been proven, which allow us to keep the number of marked nodes (needed to avoid loops in the graph traversal) to a small fraction of the total number of visited nodes.This boundary detection algorithm has been implemented. We discuss the interaction between the underlying mathematical theory and the design of the working software. We illustrate the software on some clinical studies in which the input is computed tomographic (CT) data and the output is dynamically rotating three-dimensional displays of isolated organs. Even though the medical application leads to very large scale problems, our theory and design allows us to use our method routinely on the minicomputer of a CT scanner.

This paper presents an introduction to the field of three-dimensional medical imaging It presents medical imaging terms and concepts, summarizes the basic operations performed in three-dimensional medical imaging, and describes sample algorithms for accomplishing these operations. The paper contains a synopsis of the architectures and algorithms used in eight machines to render three-dimensional medical images, with particular emphasis paid to their distinctive contributions. It compares the performance of the machines along several dimensions, including image resolution, elapsed time to form an image, imaging algorithms used in the machine, and the degree of parallehsm used in the architecture. The paper concludes with general trends for future developments in this field and references on three-dimensional medical imaging.

https://surface.syr.edu/cgi/viewcontent.cgi?article=1005&context=lcsmith_other

Three-dimensional medical imaging: algorithms and computer systems

A method for executing a query comprising a sequence of operations to be performed on one or more relational databases comprises statistically sampling the relational databases at the times the operations are to be executed and then dynamically optimizing the performance of the operations based on the statistical samples obtained as a result of the sampling step.

Gideon Frieder

Papers

The theory, design, implementation and evaluation of a three-dimensional surface detection algorithm

Back-to-Front Display of Voxel Based Objects

The theory, design, implementation and evaluation of a three-dimensional surface detection algorithm

Three-dimensional medical imaging: algorithms and computer systems

Dynamic query optimization using partial information