Journal ArticleDOI
Knowledge-based classification and tissue labeling of MR images of human brain
TLDR
The presents a knowledge-based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of the human brain that provides an accurate complete labeling of all normal tissues in the absence of large amounts of data nonuniformity.Abstract:Â
Presents a knowledge-based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of the human brain. The system consists of 2 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 landmark tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The landmark tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. The knowledge base contains information on cluster distribution in feature space and tissue models. Since tissue shapes are irregular, their models and matching are specially designed: 1) qualitative tissue models are defined for brain tissues such as white matter; 2) default reasoning is used to match a model with an MR image; that is, if there is no mismatch between a model and an image, they are taken as matched. The system has been tested with 53 slices of MR images acquired at different times by 2 different scanners. It accurately identifies abnormal slices and provides a partial labeling of the tissues. It provides an accurate complete labeling of all normal tissues in the absence of large amounts of data nonuniformity, as verified by radiologists. Thus the system can be used to provide automatic screening of slices for abnormality. It also provides a first step toward the complete description of abnormal images for use in automatic tumor volume determination. >read more
Citations
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Journal ArticleDOI
MRI segmentation: Methods and applications
Laurence P. Clarke,R.P. Velthuizen,M.A. Camacho,John J. Heine,M. Vaidyanathan,Lawrence O. Hall,R.W. Thatcher,Martin L. Silbiger +7 more
TL;DR: The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter- and intra-observer variations inherent to supervised methods.
Journal ArticleDOI
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Zhuowen Tu,Xiang Bai +1 more
TL;DR: The scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems, including high-level vision and medical image segmentation problems.
Journal ArticleDOI
Automatic tumor segmentation using knowledge-based techniques
M.C. Clark,Lawrence O. Hall,Dmitry B. Goldgof,R.P. Velthuizen,F.R. Murtagh,Martin S. Silbiger +5 more
TL;DR: A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented and generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.
Journal ArticleDOI
Fully automatic segmentation of the brain in MRI
M.S. Atkins,B.T. Mackiewich +1 more
TL;DR: A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities.
Journal ArticleDOI
MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization
TL;DR: A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed and a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically.
References
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