MRI brain lesion image detection based on color-converted K-means clustering segmentation
We present a preliminary design and experimental results of tumor objects tracking method for magnetic resonance imaging (MRI) brain images (some stock images) that utilizes color-converted segmentation algorithm with K-means clustering technique. The method is capable of solving unable exactly cont...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Published: |
Elsevier B.V.
2010
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/26284/ http://dx.doi.org/10.1016/j.measurement.2010.03.013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |
| Summary: | We present a preliminary design and experimental results of tumor objects tracking method for magnetic resonance imaging (MRI) brain images (some stock images) that utilizes color-converted segmentation algorithm with K-means clustering technique. The method is capable of solving unable exactly contoured lesion objects problem in MRI image by adding the color-based segmentation operation. The key idea of color-converted segmentation algorithm with K-means is to solve the given MRI image by converting the input gray-level image into a color space image and operating the image labeled by cluster index. In this paper we investigate the possibility of employing this approach for image-based-MRI application. The application of the proposed method for tracking tumor is demonstrated to help pathologists distinguish exactly lesion size and region. |
|---|