Fuzzy rule based clustering algorithm for medical image segmentation
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 177-180 |
| Journal / Publication | National Conference Publication - Institution of Engineers, Australia |
| Volume | 1 |
| Issue number | 94 /9 |
| Publication status | Published - 1994 |
| Externally published | Yes |
Conference
| Title | Proceedings of the International Symposium on Information Theory & Its Applications 1994. Part 1 (of 2) |
|---|---|
| City | Sydney, Aust |
| Period | 20 - 24 November 1994 |
Link(s)
Abstract
Segmentation of biological images into tissue components is becoming increasingly important in the clinical environment. One example is the segregation of brain tissues for the study of degenerative diseases. In this paper, we present a fuzzy rule based approach for classification and tissue labeling of magnetic resonance (MR) images of the human brain. K-means clustering is applied to produce clusters whose centers and variances are then used to determine fuzzy membership functions. The fuzzy rules are then directly extracted from learning examples. Our experiments show that the proposed method has a number of advantages over conventional clustering methods.
Citation Format(s)
Fuzzy rule based clustering algorithm for medical image segmentation. / Zhu, Yan; Chi, Zheri; Yan, Hong.
In: National Conference Publication - Institution of Engineers, Australia, Vol. 1, No. 94 /9, 1994, p. 177-180.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
