Automatic Segmentation of Nasopharyngeal Carcinoma from CT Images

This paper presents an automatic segmentation technique for identifying nasopharyngeal carcinoma regions in CT images. The proposed technique is based on the region growing method by which an initial seed is automatically generated. A probabilistic map representing a chance of being the tumor pixel in each CT image will be created and used for initial seed determination. This map is generated from three probabilistic functions established upon location of the tumor considered, intensities of the tumor pixels, and asymmetry of organs respectively. A representative of potential tumor pixels will be selected as an initial seed. The experimental results showed that seeds were correctly determined with the percent accuracy of 84.32%. These seeds were grown in preprocessed CT images for identifying the nasopharyngeal carcinoma regions subsequently. The results showed that, for no metastasis cases, perfect match and corresponding ratio were 85.03% and 52.44% respectively and 29.26% and 28.03% correspondingly for metastasis cases. This resulted from a single seed generated in each CT image. It was unable to identify more than one tumor region.