Segmentation of MR images using neural nets
Image and Vision Computing - Special issue: BMVC 1991
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-stage neural network for volume segmentation of medical images
Pattern Recognition Letters - special issue on pattern recognition in practice V
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Overview and fundamentals of medical image segmentation
Handbook of medical imaging
Digital Image Processing
Artificial Neural Networks for Image Understanding
Artificial Neural Networks for Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter
Digital Signal Processing
MR Images Restoration With the Use of Fuzzy Filter Having Adaptive Membership Parameters
Journal of Medical Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation
Engineering Applications of Artificial Intelligence
MRI Brain image segmentation with supervised SOM and probability-based clustering method
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
HEp-2 cell images classification based on textural and statistic features using self-organizing map
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Unsupervised neural techniques applied to MR brain image segmentation
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.