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

Using kriging for 3d medical imaging

A novel hierarchical approach toward fast parallel processing of chain-codable contours is presented. The environment, called the chain pyramid, is similar to a regular nonoverlapping image pyramid structure. The artifacts of contour processing on pyramids are eliminated by a probabilistic allocation algorithm. Building of the chain pyramid is modular, and for different applications new algorithms can be incorporated. Two applications are described: smoothing of multiscale curves and gap bridging in fragmented data. The latter is also employed for the treatment of branch points in the input contours. A preprocessing module allowing the application of the chain pyramid to raw edge data is also described. The chain pyramid makes possible fast, O(log(image/sub -/size)), computation of contour representation in discrete scale-space. >

The chain pyramid: hierarchical contour processing

PASM is a multifunction partitionable SIMD/MIMD system being designed at Purdue for parallel image understanding. It is to be a large-scale, dynamically reconfigurable multimicroprocessor system, which will incorporate over 1,000 complex processing elements. Parallel algorithm studies and simulations have been used to analyze application tasks in order to guide design decisions. A prototype of PASM is under construction (funded by an equipment grant from IBM), including 30 Motorola MC68010 processors, a multistage interconnection network, five disk drives, and connections to the Purdue Engineering Computer Network (for access to peripherals, terminals, software development tools, etc.). PASM is to serve as a vehicle for studying the use of parallelism for performing the numeric and symbolic processing needed for tasks such as computer vision. The PASM design concepts and prototype are overviewed and brief examples of parallel algorithms are given.

PASM: a reconfigurable parallel system for image processing

Computer vision tasks require an enormous amount of computation, especially when the data is in image form, demanding high-performance computers for practical, real-time applications. Parallelism appears to be the only economical way to achieve the level of performance required for vision tasks. Researchers in human and machine vision share the belief that massive parallel processing characterizes low-level vision. In this paper we review the various parallel algorithms used in Computer Vision. The problem of visual recognition is divided into three conceptual levels-low-level, intermediate-level and high-level. There are few conceptual difficulties in parallelizing low-level vision. Intermediate-level vision is relatively difficult. Most algorithms in these two levels have been parallelized. However, not much work has been done in high-level vision. We present a survey of algorithms within each of the three levels.

Parallelism in computer vision: a review

Contour extraction is used as an image processing scenario to explore the advantages of parallelism and the architectural requirements for a parallel computer system, such as PASM. Parallel forms of edge-guided thresholding and contour tracing algorithms are developed and analyzed to highlight important aspects of the scenario. Edge-guided thresholding uses adaptive thresholding to allow contour extraction where gray level variations would not allow global thresholding to be effective. Parallel techniques are shown to eliminate some types of overhead associated with serial processing, offer the possibility of improved algorithm capability and accuracy, and decrease execution time. The implications that the parallel scenario has for machine architecture are considered. Various desirable system attributes are established. 30 references.

O.R. Mitchell

Papers

Parallel algorithm for contour extraction: advantages and architectural implications