Unsupervised tissue type segmentation of 3D dual-echo MR head data
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Automating Segmentation of Dual-Echo MR Head Data
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
Efficient content-based indexing of large image databases
ACM Transactions on Information Systems (TOIS)
Signature-Based Indexing for Retrieval by Spatial Content in Large 2D-String Image Databases
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Inter-patient analysis of tomographic data
CBMS '97 Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems (CBMS '97)
Feature extraction of brain CT image based on target shape
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Certain investigation on MRI segmentation for the implementation of CAD system
WSEAS Transactions on Computers
A robust statistical method for brain magnetic resonance image segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
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A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented. The algorithm consists of a sequence of adaptive histogram analysis, morphological operations and knowledge based rules to accurately classify various regions such as the brain matter and the cerebrospinal fluid, and detect if there are any abnormal regions. It can be completely automated and has been tested on over hundred images from several patient studies. Experimental results are provided.