19 research outputs found
Internal Fixation of the Lumbar Spine: Further Clinical Experience Using Computer Assisted Design and Manufacture of a Precise System
Diagnostic contribution of 3-dimensional reconstruction using x-ray computed tomography: sections and surfaces of the anatomy of the head
Multivariate Statistical Model for 3D Image Segmentation with Application to Medical Images
In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms)
Three-dimensional computed tomography in acute cervical spine trauma: a preliminary report
Association of multiple vertebral hemangiomas and severe paraparesis in a patient with a PTEN
Addressing the Coming Radiology Crisis—The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process (TRIP™) Initiative
The Society for Computer Applications in Radiology (SCAR) Transforming the Radiological Interpretation Process (TRIPâ„¢) Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster interdisciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. TRIPâ„¢ will focus on the following basic objectives: improving the efficiency of interpretation of large data sets, improving the timeliness and effectiveness of communication, and decreasing medical errors. The ultimate goal of the initiative is to improve the quality and safety of patient care. Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved are human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care