Self-Configuring and Evolving Fuzzy Image Thresholding

Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results. Evolving fuzzy systems for adjustment of segmentation parameters have been proposed recently (Evolving fuzzy image segmentation -- EFIS [1]). However, similar to any other algorithm, EFIS too suffers from a few limitations when used in practice. As a major drawback, EFIS depends on detection of the object of interest for feature calculation, a task that is highly application-dependent. In this paper, a new version of EFIS is proposed to overcome these limitations. The new EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to auto-configure the parameters that are fixed in EFIS. As well, the proposed SCEFIS relies on a feature selection process that does not require the detection of a region of interest (ROI).

[1]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[2]  Hamid R. Tizhoosh,et al.  Image thresholding using type II fuzzy sets , 2005, Pattern Recognit..

[3]  Wayne Niblack,et al.  An introduction to digital image processing , 1986 .

[4]  C. A. Murthy,et al.  Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Deng Cai,et al.  Unsupervised feature selection for multi-cluster data , 2010, KDD.

[6]  Huan Liu,et al.  Spectral feature selection for supervised and unsupervised learning , 2007, ICML '07.

[7]  Mohamed S. Kamel,et al.  Efficient greedy feature selection for unsupervised learning , 2012, Knowledge and Information Systems.

[8]  Shahryar Rahnamayan,et al.  Image thresholding using micro opposition-based Differential Evolution (Micro-ODE) , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[9]  Hamid R. Tizhoosh,et al.  Evolving fuzzy image segmentation , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[10]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[11]  Mao-Jiun J. Wang,et al.  Image thresholding by minimizing the measures of fuzzines , 1995, Pattern Recognit..

[12]  Hamid R. Tizhoosh,et al.  EFIS—Evolving Fuzzy Image Segmentation , 2014, IEEE Transactions on Fuzzy Systems.

[13]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[14]  Hamid R. Tizhoosh,et al.  Quasi-global oppositional fuzzy thresholding , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[15]  Hamid R. Tizhoosh,et al.  Using reinforcement learning for image thresholding , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[16]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[17]  Hamid R. Tizhoosh,et al.  Type II Fuzzy Image Segmentation , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[18]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .