|
| (1) Input: MR image |
| (2) Output: Segmented tumor image |
| (3) Preprocessing: Perform adaptive Wiener filter and |
| morphological operation. |
| (4) Set the value of clusters k, the degree of fuzziness , the |
| error , and the value of objective function |
| (5) Initialize the cluster centroid using K-means++: |
| (6) Choose an initial center at random from image R, where x represents the pixel of the MR image |
| (7) Begin |
| (8) Calculate the probability of each remaining pixel using |
| , where is the distance between the |
| pixel and the nearest center, ’ represents the next pixel. |
| (9) Choose the pixel with the highest probability as the next |
| initial center |
| (10) Update the the i-th cluster centroid |
| (11) If k initial centers are calculated |
| (12) Then Break |
| (13) End if |
| (14) Cluster the obtained images using K++GKFCM: |
| (15) Begin: |
| (16) Calculate K(x, y) using Eq. (9) |
| (17) Update the membership degree using Eq. (5) |
| (18) Update the the i-th cluster centroid using Eq. (4) |
| (19) If , where represents |
| the function of the i-th iteration |
| (20) Then Break |
| (21) End if |
| (22) End |
|
