The benign and Malignant Recognition System of Nasopharynx in MRI image with Neural-Fuzzy based Adaboost classifier

The current state of diagnosis of nasopharyngeal approach to image interpretation is artificial, but this way will be sentenced to cross-cultural experience for each practice discrimination and may lead to refractory time delay. We used the image taken from radiologists and applied the Unsharp Mask to enhance the image edge. In order to reduce the computation time and increase the efficiency, the doctor may specify an ellipse region of interest (ROIs) of nasopharynx in the image. After that, we use histogram equalization to denoise and use Otsu methods to obtain the threshold value to binaries it in this region. After performing the above two methods, we can successfully segment the tumor region of the nasopharynx. Then, we use texture and geometric feature extraction method to extract the tumor region feature, and trained the data by Neural-Fuzzy based AdaBoost classifier to recognize benign or malignant tumors of the nasopharynx. This paper hope that it can improve nasopharyngeal malignant cancer identification accuracy, and assist doctors make more accurate diagnosis through using tumor texture, hypertrophy and symmetrical distribution features in Neural-Fuzzy based Adaboost classifier.