Tumor Diagnosis Based on the GMM Feature Decision Classification of Brain MR Images

다국어 초록 (Multilingual Abstract)

The MR image has provided lots of information used for medical examination. Accurate and robust brain MR image segmentation, feature extraction and feature classification are very important for clinical tumor diagnosis. A new tumor diagnosis method based on brain MR images is hereby put forward. Firstly, detect the deformed area of the images through multi-threshold segmentation morphology, and then, extract the GMM feature used for the classification, and finally, classify the types of tumor images by using decision tree classifier. The whole classification consists of two stages, during training stage extract the different features of tumor images and non-tumor images, and during testing stage conduct the classification of tumor and non-tumor based on knowledge databank. The computing method is appraised according to the three performance index including accuracy, false alarm rate and loss detecting rate, the experiment results show that the computing function is excellent and is helpful for better brain tumor diagnosis.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. This Article’s Methods
• 2.1. Multi-Threshold Segmentation
• 2.2. GMM Feature Extraction
• 2.3. Decision Tree Classifier
• 3. Experiment Result and Analysis
• 4. Conclusion
• Acknowledgement
• References