%0 Journal Article %1 das2010fuzzy %A S R Kannan, S Ramathilagam, R Pandiyarajan %A Sathya, A %D 2010 %E Das, Dr. VInu V %J International Journal on Signal & Image Processing %K Bias_field Data_analysis FCM MRI Segmentation %N 2 %P 5 %T Fuzzy clustering Approach in segmentation of T1-T2 brain MRI %U http://doi.searchdl.org/01.IJSIP.1.2.08 %V 1 %X Segmentation is a difficult and challenging problem in the magnetic resonance images, and it considered as important in computer vision and artificial intelligence. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. In this paper, we present a novel FCM algorithm for weighted bias (also called intensity in-homogeneities) estimation and segmentation of MRI. Normally, the intensity inhomogeneities are attributed to imperfections in the radio-frequency coils or to the problems associated with the image acquisition. Our algorithm is formulated by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. Further this paper proposes a center knowledge method in order to reduce the running time of proposed algorithm. The proposed method can deal with the intensity in-homogeneities and image noise effectively. We have compared our results with other reported methods. The results using real MRI data show that our method provides better results compared to standard FCM based algorithms and other modified FCM-based techniques.

@article{das2010fuzzy, abstract = {Segmentation is a difficult and challenging problem in the magnetic resonance images, and it considered as important in computer vision and artificial intelligence. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. In this paper, we present a novel FCM algorithm for weighted bias (also called intensity in-homogeneities) estimation and segmentation of MRI. Normally, the intensity inhomogeneities are attributed to imperfections in the radio-frequency coils or to the problems associated with the image acquisition. Our algorithm is formulated by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. Further this paper proposes a center knowledge method in order to reduce the running time of proposed algorithm. The proposed method can deal with the intensity in-homogeneities and image noise effectively. We have compared our results with other reported methods. The results using real MRI data show that our method provides better results compared to standard FCM based algorithms and other modified FCM-based techniques.}, added-at = {2012-10-03T10:07:59.000+0200}, author = {{S R Kannan, S Ramathilagam}, R Pandiyarajan and Sathya, A}, biburl = {https://www.bibsonomy.org/bibtex/2cd1741cf1cbeedf0740af69534189d82/ideseditor}, editor = {Das, Dr. VInu V}, interhash = {3466a81e840f33b1d60086297ed0f7b9}, intrahash = {cd1741cf1cbeedf0740af69534189d82}, journal = {International Journal on Signal & Image Processing }, keywords = {Bias_field Data_analysis FCM MRI Segmentation}, month = {July}, number = 2, pages = 5, timestamp = {2012-10-03T10:07:59.000+0200}, title = {Fuzzy clustering Approach in segmentation of T1-T2 brain MRI}, url = {http://doi.searchdl.org/01.IJSIP.1.2.08}, volume = 1, year = 2010 }