Resumen:
Brain tumors are a serious and death-defying disease for human life. Discovering an appropriate brain tumor image from a magnetic resonance imaging (MRI) archive is a challenging job for the radiologist. Most search engines retrieve images on the basis of traditional text-based approaches. The main challenge in the MRI image analysis is that low-level visual information captured by the MRI machine and the high-level information identified by the assessor. This semantic gap is addressed in this study by designing a new feature extraction technique. In this paper, we introduce Content-Based Medical Image retrieval (CBMIR) system for retrieval of brain tumor images from the large data. Firstly, we remove noise from MRI images employing several filtering techniques. Afterward, we design a feature extraction scheme combining Gabor filtering technique (which is mainly focused on specific frequency content at the image region) and Walsh-Hadamard transform (WHT) (conquer technique for easy configuration of image) for discovering representative features from MRI images. After that, for retrieving the accurate and reliable image, we employ Fuzzy C-Means clustering Minkowski distance metric that can evaluate the similarity between the query image and database images. The proposed methodology design was tested on a publicly available brain tumor MRI image database. The experimental results demonstrate that our proposed approach outperforms most of the existing techniques like Gabor, wavelet, and Hough transform in detecting brain tumors and also take less time. The proposed approach will be beneficial for radiologists and also for technologists to build an automatic decision support system that will produce reproducible and objective results with high accuracy.
