Two-dimensional extension of variance-based thresholding for image segmentation

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and two-dimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-dimensional space in the proposed scheme firstly, and then the variance-based criterion is constructed for threshold selection. The experimental results on bi-level and multilevel thresholding for synthetic and real-world images demonstrate the success of the proposed image thresholding scheme, as compared with the Otsu method, the two-dimensional Otsu method and the minimum class variance thresholding method.

[1]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[2]  Yu Qiao,et al.  Thresholding based on variance and intensity contrast , 2007, Pattern Recognit..

[3]  Darrel L. Chenoweth,et al.  Two-dimensional entropic segmentation , 1999, Pattern Recognit. Lett..

[4]  Zhao Feng,et al.  Two-Dimensional Otsu's Curve Thresholding Segmentation Method for Gray-Level Images , 2007 .

[5]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[6]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[7]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[8]  Edward Roy Davies,et al.  The use of variance and entropic thresholding methods for image segmentation , 1995, Pattern Recognit..

[9]  Thomas A. Albert,et al.  Threshold selection using a minimal histogram entropy difference , 1997 .

[10]  Juliana Gambini,et al.  Polarimetric SAR image segmentation with B-splines and a new statistical model , 2010, Multidimens. Syst. Signal Process..

[11]  Qingmao Hu,et al.  On minimum variance thresholding , 2006, Pattern Recognit. Lett..

[12]  Richard C. Staunton,et al.  A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns , 2007, Pattern Recognit..

[13]  Mateu Sbert,et al.  Image Segmentation Using Excess Entropy , 2009, J. Signal Process. Syst..

[14]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[15]  Weinan Chen,et al.  Fast recursive algorithms for two-dimensional thresholding , 1998, Pattern Recognit..