MRI segmentation of Medical images using FCM with initialized class centers via genetic algorithm

Image segmentation is a critical stage in many computer vision and image process applications. Accurate segmentation of medical images is very essential in Medical applications but it is very difficult job due to noise and in homogeneity. Fuzzy C-Mean (FCM) is one of the most popular Medical image clustering methods. We noticed that for some images, FCM is sensitive to initialization of centre of clusters. This article introduced a new method based on the combination of genetic algorithm and FCM to solve this problem. The genetic algorithm is used to find initialized centre of the clusters. In this method, the centre is obtained by minimizing an object Function. This object Function specifies sum of distances between each data and their cluster centres. Then FCM is applied with to the case. The experimental result demonstrates the effectiveness of new method by able to initialize centre of the clusters.

[1]  Scott T. Acton,et al.  Scale space classification using area morphology , 2000, IEEE Trans. Image Process..

[2]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[3]  Wei-Guang Teng,et al.  Exploiting the Self-Organizing Map for Medical Image Segmentation , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[4]  Yun Zhang,et al.  Hierarchical Classification for Imbalanced Multiple Classes in Machine Vision Inspection , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[5]  Haijun Liao,et al.  Image Segmentation on Colonies Images by A Combined Algorithm of Simulated Annealing and Genetic Algorithm , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[6]  P. Babyn,et al.  X-ray image segmentation using active contour model with global constraints , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.

[7]  Y. A. Tolias,et al.  On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system , 1998, IEEE Signal Processing Letters.

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

[9]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[10]  Rajesh N. Davé,et al.  Characterization and detection of noise in clustering , 1991, Pattern Recognit. Lett..

[11]  J. Jan.  Medical Image Processing, Reconstruction and Restoration: Concepts and Methods , 2005 .

[12]  Dao-Qiang Zhang,et al.  A novel kernelized fuzzy C-means algorithm with application in medical image segmentation , 2004, Artif. Intell. Medicine.