Segmentation of CT Brain Images Using Unsupervised Clusterings
Journal of Visualization
Automated Segmentation and Retrieval System for CT Head Images
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
A generalized spatial fuzzy C-means algorithm for medical image segmentation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An automatic segmentation method for scanned images of wheat root systems with dark discolourations
International Journal of Applied Mathematics and Computer Science - Special Section: Robot Control Theory Cezary Zielinski
Towards patient-specific anatomical model generation for finite element-based surgical simulation
IS4TM'03 Proceedings of the 2003 international conference on Surgery simulation and soft tissue modeling
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
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
Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies
Applied Soft Computing
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov random field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.