A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method
- 7 March 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Cybernetics
- Vol. 43 (3), 910-920
- https://doi.org/10.1109/tsmcb.2012.2218233
Abstract
In the last decades, due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, we first designed an energy functional based on the fuzzy c -means objective function which incorporates the bias field that accounts for the intensity inhomogeneity of the real-world image. Using the gradient descent method, we obtained the corresponding level set equation from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. The method is fast, robust against noise, independent to the position of the initial contour, effective in the presence of intensity inhomogeneity, highly parallelizable and can detect objects with or without edges. Experiments on medical and real-world images demonstrate the performance of the proposed method in terms of speed and efficiency.Keywords
This publication has 30 references indexed in Scilit:
- Image multi-thresholding by combining the lattice Boltzmann model and a localized level set algorithmNeurocomputing, 2012
- Lattice Boltzmann Method of Active Contour for Image SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- A tree-structured framework for purifying “complex” clusters with structural roles of individual dataPattern Recognition, 2010
- A Relay Level Set Method for Automatic Image SegmentationIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010
- Distance Regularized Level Set Evolution and Its Application to Image SegmentationIEEE Transactions on Image Processing, 2010
- A Unified Tensor Level Set for Image SegmentationIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010
- An evaluation metric for image segmentation of multiple objectsImage and Vision Computing, 2009
- From contours to regions: An empirical evaluationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Cellular Automata based Level Set Method for Image SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statisticsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002