TY - GEN
T1 - N-cuts parameter adjustment using evolving fuzzy inferencing
AU - Othman, Ahmed A.
AU - Tizhoosh, Hamid R.
PY - 2013
Y1 - 2013
N2 - Normalized cut (N-cut) is a rather recent approach to image segmentation representing the image as a graph and using eigenvalues to partition it. However, this method has several parameters that affect the segmentation accuracy. Using pre-set values for these parameters may generate good results for some images and bad results for others. Thus, to achieve maximum segmentation accuracy, these parameters may be manually fine-tuned for every set of images. This process, of course, would be impractical and lack generality. In this paper, a method is proposed to automatically determine N-cut parameters for every single image based on the image features using evolving fuzzy sets. The proposed method is applied to magnetic reasoning images (MRI) of bladder.
AB - Normalized cut (N-cut) is a rather recent approach to image segmentation representing the image as a graph and using eigenvalues to partition it. However, this method has several parameters that affect the segmentation accuracy. Using pre-set values for these parameters may generate good results for some images and bad results for others. Thus, to achieve maximum segmentation accuracy, these parameters may be manually fine-tuned for every set of images. This process, of course, would be impractical and lack generality. In this paper, a method is proposed to automatically determine N-cut parameters for every single image based on the image features using evolving fuzzy sets. The proposed method is applied to magnetic reasoning images (MRI) of bladder.
UR - http://www.scopus.com/inward/record.url?scp=84887843766&partnerID=8YFLogxK
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U2 - 10.1109/FUZZ-IEEE.2013.6622480
DO - 10.1109/FUZZ-IEEE.2013.6622480
M3 - Conference contribution
AN - SCOPUS:84887843766
SN - 9781479900220
T3 - IEEE International Conference on Fuzzy Systems
BT - FUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
T2 - 2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Y2 - 7 July 2013 through 10 July 2013
ER -