N-cuts parameter adjustment using evolving fuzzy inferencing

Artificial Intelligence and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English (US)
Title of host publication	FUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2013.6622480
State	Published - 2013
Event	2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India Duration: Jul 7 2013 → Jul 10 2013

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Conference

Conference	2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Country/Territory	India
City	Hyderabad
Period	7/7/13 → 7/10/13

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/FUZZ-IEEE.2013.6622480

Cite this

@inproceedings{66258cb9c7fa4c7696633b78939f81c7,

title = "N-cuts parameter adjustment using evolving fuzzy inferencing",

abstract = "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.",

author = "Othman, {Ahmed A.} and Tizhoosh, {Hamid R.}",

year = "2013",

doi = "10.1109/FUZZ-IEEE.2013.6622480",

language = "English (US)",

isbn = "9781479900220",

series = "IEEE International Conference on Fuzzy Systems",

booktitle = "FUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems",

note = "2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 ; Conference date: 07-07-2013 Through 10-07-2013",

}

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