Evolving Fuzzy Image Segmentation with Self-Configuration

Current image segmentation techniques usually require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach, especially when a large number of images must be processed sequentially in daily practice. The use of evolving fuzzy systems for designing a method that automatically adjusts parameters to segment medical images according to the quality expectation of expert users has been proposed recently (Evolving fuzzy image segmentation EFIS). However, EFIS suffers from a few limitations when used in practice mainly due to some fixed parameters. For instance, EFIS depends on auto-detection of the object of interest for feature calculation, a task that is highly application-dependent. This shortcoming limits the applicability of EFIS, which was proposed with the ultimate goal of offering a generic but adjustable segmentation scheme. 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 self-estimate the parameters that are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection process that does not require auto-detection of an ROI. The proposed SC-EFIS was evaluated using the same segmentation algorithms and the same dataset as for EFIS. The results show that SC-EFIS can provide the same results as EFIS but with a higher level of automation.

[1]  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.

[2]  Huan Liu,et al.  Semi-supervised Feature Selection via Spectral Analysis , 2007, SDM.

[3]  Michel Verleysen,et al.  A graph Laplacian based approach to semi-supervised feature selection for regression problems , 2013, Neurocomputing.

[4]  Jason Weston,et al.  Embedded Methods , 2006, Feature Extraction.

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

[6]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Amparo Alonso-Betanzos,et al.  Filter Methods for Feature Selection - A Comparative Study , 2007, IDEAL.

[8]  Denis Hamad,et al.  Constraint scores for semi-supervised feature selection: A comparative study , 2011, Pattern Recognit. Lett..

[9]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[11]  E.M. Arvacheh,et al.  Pattern Analysis Using Zernike Moments , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

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

[13]  Ruichu Cai,et al.  BASSUM: A Bayesian semi-supervised method for classification feature selection , 2011, Pattern Recognit..

[14]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[16]  Haytham Elghazel,et al.  A semi-supervised feature ranking method with ensemble learning , 2012, Pattern Recognit. Lett..

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

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

[19]  Lluís A. Belanche Muñoz,et al.  Feature selection algorithms: a survey and experimental evaluation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[20]  Le Song,et al.  Supervised feature selection via dependence estimation , 2007, ICML '07.

[21]  M. Carmen Garrido,et al.  Feature subset selection Filter-Wrapper based on low quality data , 2013, Expert Syst. Appl..

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

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

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

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

[26]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

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

[28]  Filiberto Pla,et al.  Supervised feature selection by clustering using conditional mutual information-based distances , 2010, Pattern Recognit..