MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

By Shan Shen, William Sandham, Malcolm H. Granat, Annette Sterr. IEEE Transactions on Information Technology in Biomedicine, 2005

473 citations recorded by the source corpus.

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/48063bfcb7b3f62edcd135ab07b5cdc4ea8eb94a

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Citations in the corpus

Medical image analysis with artificial neural networks (2010, methodology): “[22] proposed a segmentation technique based on an extension to the”
A survey of MRI-based brain tumor segmentation methods (2014, background): “Removal of the skull region reduces the chances of misclassifying diseased tissues [24] .”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, background): “On the other hand, to keep the continuity from the FCM algorithm, Shen et al. [80] introduced a new similarity measure that depends on spatial neighbourhood information, where the degree of the neighbourhood attraction is optimized by a neural network.”
Automated medical image segmentation techniques (2010, methodology): “[66,67] Further, for this segmentation, techniques based on statistical methods[68,69] and fuzzy methods,[58,70-73] which gives soft segmentation results are particularly useful in overcoming the above mentioned limitation.”