Please use this identifier to cite or link to this item:
https://ptsldigital.ukm.my/jspui/handle/123456789/395187| Title: | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| Authors: | M A. Balafar Abd Rahman Ramli M.Iqbal Saripan Rozi Mahmud Syahmsiah Mashohor Hakimeh Balafar |
| Conference Name: | International Symposium on Information Technology |
| Keywords: | Fuzzy C-Mean (FCM) Magnetic resonance imaging (MRI) Image segmentation |
| Conference Date: | 26/08/2008 |
| Conference Location: | Kuala Lumpur Convention Centre |
| Abstract: | 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. |
| Pages: | 4 |
| Call Number: | T58.5.C634 2008 kat sem j.4 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE),Piscataway, US |
| Appears in Collections: | Seminar Papers/ Proceedings / Kertas Kerja Seminar/ Prosiding |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.