Fuzzy Image Segmentation: An Automatic Unsupervised Method

2014 ◽  
pp. 65-88 ◽  
Author(s):  
Silvana G Dellepiane ◽  
Sonia Nardotto
Keyword(s):  
Image Segmentation ◽  
Unsupervised Method ◽  
Fuzzy Image

An Unsupervised Method for Histological Image Segmentation based on Tissue Cluster Level Graph Cut

2021 ◽  
pp. 101974
Author(s):  
Hongming Xu ◽  
Lina Liu ◽  
Xiujuan Lei ◽  
Mrinal Mandal ◽  
Cheng Lu
Keyword(s):  
Image Segmentation ◽  
Graph Cut ◽  
Histological Image ◽  
Unsupervised Method ◽  
Cluster Level

scholarly journals Fuzzy Image Segmentation Using Shape Information

2005 ◽  
Author(s):  
M.A. Ali ◽  
G.C. Karmakar ◽  
L.S. Dooley
Keyword(s):  
Image Segmentation ◽  
Shape Information ◽  
Fuzzy Image

Review of Fuzzy Image Segmentation Techniques

2011 ◽  
pp. 282-313 ◽  
Author(s):  
Gour C. Karmakar ◽  
Laurence Dooley ◽  
Mahbubhur Rahman Syed
Keyword(s):  
Image Segmentation ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Clustering Methods ◽  
Comprehensive Overview ◽  
Domain Specific ◽  
Soft Computing Techniques ◽  
Fuzzy Clustering Methods ◽  
Fuzzy Image ◽  
Multimodal Images

This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

scholarly journals Fuzzy Image Segmentation Algorithms in Wavelet Domain

10.5772/36336 ◽  
2012 ◽  
Author(s):  
Heydy Castillejos ◽  
Volodymyr Ponomaryov
Keyword(s):  
Image Segmentation ◽  
Wavelet Domain ◽  
Segmentation Algorithms ◽  
Fuzzy Image

scholarly journals Fuzzy Image Segmentation Using Validity Indexes Correlation

2015 ◽  
Vol 7 (3) ◽  
pp. 15-26 ◽  
Author(s):  
Daniel Fernando Tello Gamarra
Keyword(s):  
Image Segmentation ◽  
Fuzzy Image

Evolving fuzzy image segmentation

2011 ◽  
Author(s):  
Ahmed A. Othman ◽  
Hamid R. Tizhoosh
Keyword(s):  
Image Segmentation ◽  
Fuzzy Image
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