Jiayin ZHOU^*, Tuan-Kay LIM, Vincent CHONG¹ and Jing HUANG¹

School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
¹Department of Diagnostic Radiology, Singapore General Hospital, Outram Road, Singapore

(Received February 10, 2003; Accepted May 1, 2003)

Tumor segmentation from magnetic resonance imaging (MRI) is important for volume estimation and visualization of nasopharyngeal carcinoma (NPC). In some cases, segmentation using the general multispectral (GM) method often obtained poor results due to the high false positives caused by complex anatomic structures and serious overlap in feature space. In this study, a texture combined multispectral fuzzy clustering (TCMFC) segmentation algorithm was proposed. A texture measure of T1-weighted (T1) MR image was introduced by calculating the two-order central statistical information of every pixel within a window after the window convolution operation. The texture measure and the intensities in T1 and contrast-enhanced T1 images formed the new 3-D feature vector for fuzzy clustering implemented by semi-supervised fuzzy c-means (SFCM). Testing showed that by reducing the false positives significantly, the TCMFC method achieved improved segmentation results, compared with the GM method.

Key words: multispectral image segmentation, texture analysis, image processing, MRI, nasopharyngeal carcinoma

^*Corresponding author. present address: Biomedical Engineering Research Centre, Nanyang Technological University, Nanyang Avenue, Singapore 639798. E-mail address: P149624472@ntu.edu.sg