Abstract
This paper presents a knowledge based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of human brain. The system consists of two components: an unsupervised clustering algorithm and an expert system. MR brain data is initially segmented by the unsupervised algorithm, then, the expert system locates a focus-of- attention tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The focus-of-attention tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. Abnormal slices are labeled by reclustering regions of interest with knowledge accumulated from previous analysis. The domain knowledge contains tissue distribution in feature space acquired with a clustering algorithm, and tissue models. Default reasoning is used to match a qualitative model with its instances. The system has been tested with fifty-three slices acquired at different times by two different scanners.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
