An expert system based on texture features and decision tree classifier for diagnosis of tumor in brain MR images
G. Kharmega Sundararaj, Velan Balamurugan · 2014
In this paper a new tumor classification system has been designed and developed for MRI systems. The MR imaging is a mostly used scheme for high excellence in medical imaging, it gives clear imageing capability especially in brain imaging where the soft-tissues contrast and non invasiveness is a clear advantage. The proposed method consists of three stages namely pre-processing, feature extraction and classification. In the first stage, gausian filter is applied for extracting the noise for experimental image. In the second stage, Statistical texture features are extracted for the purpose of classification. Finally, the decision tree classifier is used to classify the type of tumor image. In our proposed system classification has two divisions: i) training stage and ii) testing stage. In the training stage, various features are extracted from the tumor and non tumor images. In testing stage, based on the knowledge base, the classifier classify the image into tumor and non- tumor. Thus, the proposed system has been evaluated on a dataset of 40 patients. The proposed system was found efficient in classification with a success of more than 95% of accuracy.