Using Fuzzy Information in Knowledge Guided Segmentation of Brain Tumors

This paper presents a system that integrates a knowledge-based system with unsupervised fuzzy clustering to automatically segment and label glioblastoma multiforme tumors in magnetic resonance slices of the human brain. Each slice is initially segmented by an unsupervised fuzzy c-means algorithm. The segmented image, along with a set of tissue cluster centers and some knowledge gathered during “pre-processing,” is then given to the rule-based system which uses model-based recognition techniques and further fuzzy clustering to iteratively locate tissues of interest. These “focus-of-attention” tissues are analyzed by matching them with expected characteristics. Further fuzzy reclustering is aided by the use of initialization and training data created by the knowledge system.