Automatic retrival of MRI brain image using multiqueries system

CBIR technique is becoming increasingly important in medical field in order to store, manage, and retrieve image data based on user query. Searching is done by means of matching the image features such as texture, shape or different combinations of them. Texture features play an important role in computer vision, image processing and pattern recognition. In this paper we introduce a novel method of using SVM (Support Vector machine) classifier followed by KNN (K-nearest neighbour) for CBIR using texture and shape feature. We propose a robust retrieval using a supervised classifier which concentrates on extracted features. Gray level co-occurance matrix algorithm is implemented to extract the texture features from images. The feature optimization is done on the extracted features to select best features out of it to train the classifier. The classification is performed on the dataset and it is classified into three categories such as normal, benign and malignant. The query image is classified by the classifier to a particular class and the relevant images are retrieved from the database. To improve accuracy to calculate the precision value and recall in relevant image. Furthermore no of tissues stage storage in database to get relevant image in different feature extraction method.