Magnetic resonance (MR) imaging is frequently used to diagnose abnormalities in the spinal intervertebral discs. Owing to the non-isotropic resolution of typical MR spinal scans, physicians prefer to align the scanner plane with the disc in order to maximize the diagnostic value and to facilitate comparison with prior and follow-up studies. Commonly a planning scan is acquired of the whole spine, followed by a diagnostic scan aligned with selected discs of interest. Manual determination of the optimal disc plane is tedious and prone to operator variation. A fast and accurate method to automatically determine the disc alignment can decrease examination time and increase the reliability of diagnosis. We present a validation study of an automatic spine alignment system for determining the orientation of intervertebral discs in MR studies. In order to measure the effectiveness of the automatic alignment system, we compared its performance with human observers. 12 MR spinal scans of adult spines were tested. Two observers independently indicated the intervertebral plane for each disc, and then repeated the procedure on another day, in order to determine the inter- and intra-observer variability associated with manual alignment. Results were also collected for the observers utilizing the automatic spine alignment system, in order to determine the method's consistency and its accuracy with respect to human observers. We found that the results from the automatic alignment system are comparable with the alignment determined by human observers, with the computer showing greater speed and consistency.
This paper presents a data fusion-based approach to designing an Automated Fingerprint Identification System (AFIS). Fingerprint matching methods vary from pattern matching, using ridge structure, orientation, or even the entire fingerprint itself, to point critical matching, using localized features such as ridge discontinuities, e.g. minutiae, or porous structures. Localized matching methods, such as minutiae, tend to yield more compact templates, in general, than pattern based methods. However, the reliability of localized features may be an issue, since they are affected adversely by the quality of the captured fingerprint, i.e. the degree of noise. Minutiae-based matching methods tend to be slower, albeit more accurate, than pattern-based methods. The trade-off in designing a cost-effective AFIS in terms of processing power (CPU) used, matching speed, and accuracy, lies in the choice of the proper matching methods that are selected to optimize performance by maximizing the matching accuracy while minimizing the search time. In this paper we present a systematic design and study of a fusion-based AFIS using a multiplicity of matching methods to optimize system performance and minimize required CPU cost.
This paper describes the Ver-i-Fus Integrated Access Control and Information Monitoring and Management (IAC-I2M) system that INTELNET Inc. has developed. The Ver-i-Fus IAC-I2M system has been designed to meet the most stringent security and information monitoring requirements while allowing two- way communication between the user and the system. The systems offers a flexible interface that permits to integrate practically any sensing device, or combination of sensing devices, including a live-scan fingerprint reader, thus providing biometrics verification for enhanced security. Different configurations of the system provide solutions to different sets of access control problems. The re-configurable hardware interface, tied together with biometrics verification and a flexible interface that allows to integrate Ver-i-Fus with an MIS, provide an integrated solution to security, time and attendance, labor monitoring, production monitoring, and payroll applications.
Automated Fingerprint Identification System (AFIS) provide a means for non-manual fingerprint database searches. Future AFIS applications demand greater fingerprint match request throughput, and for larger fingerprint databases. Barriers to the implementation of a high volume AFIS are analyzed. Data-fusion methods are proposed as a method to maximize integration of fingerprint feature information with limited computational resources. Preliminary results from a prototype AFIS system are presented.