A Novel Level Set Method with Improved Fuzzy C-Means Based on Genetic Algorithm for Image Segmentation

The level set method has many advantages for image segmentation, such as dealing with sharp corners, segmenting the boundaries of complex object and so on. However, it is easily affected by initial contour and control parameters. Fuzzy C-Means (FCM) clustering algorithm is one of the most popular methods of clustering analysis. Nevertheless, the traditional FCM clustering algorithm does not work well, because its initial centers are chosen randomly. In this paper, with the help of Genetic Algorithm (GA), we get the optimized cluster centers. For images, the resulting fuzzy clustering is used as the initial level set contour. At the same time, the results of fuzzy clustering can reduce the controlling parameters of level set method. The experiment results confirm its advantages for image segmentation.

[1]  Jun Duan,et al.  Development of an accelerated GVF semi-automatic contouring algorithm for radiotherapy treatment planning , 2009, Comput. Biol. Medicine.

[2]  Yi Shen,et al.  Fuzzy c-means clustering based on spatial neighborhood information for image segmentation , 2010 .

[3]  Xuelong Li,et al.  An Efficient MRF Embedded Level Set Method for Image Segmentation , 2015, IEEE Transactions on Image Processing.

[4]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[5]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[6]  Sadaaki Miyamoto,et al.  Possibilistic Approach to Kernel-Based Fuzzy c-Means Clustering with Entropy Regularization , 2005, MDAI.

[7]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[8]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[9]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[10]  N. Paragios,et al.  A level set approach for shape-driven segmentation and tracking of the left ventricle , 2003, IEEE Transactions on Medical Imaging.

[11]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[12]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.

[14]  Parvaneh Saeedi,et al.  Automatic Segmentation of Trophectoderm in Microscopic Images of Human Blastocysts , 2015, IEEE Transactions on Biomedical Engineering.