CBIR aided classification using extreme learning machine with probabilistic scaling in MRI brain image

Abstract

Abstract An enormous number of magnetic resonance imaging (MRI) brain images were produced in hospitals and several MRI centers. To exploit the diagnosis in MRI brain image, “content-based image retrieval (CBIR)” system is accessed in the MRI brain image database. In this paper, a content-based MRI brain image retrieval system is presented, which is helpful in the medical field to seek a diagnosis in an MRI brain image that is similar to the example given. This paper consists of preprocessing, feature extraction, feature selection, similarity measure, and classification. In the preprocessing phase, the Wiener filter is used to remove the unwanted pixels from an MRI brain image. In the second phase, the features related to MRI brain image are extracted using characteristics of shape, margin, and density of the MRI. In the third stage, the features of MRI brain image were reduced using principal component analysis. CBIR classification is used in this method to gain effectual results. In the first stage, retrieval images are obtained using similarity measures using the similarity between the query image features and the derived trained image features. Finally, the classification stage is an extreme learning machine with probabilistic scaling used to classify the obtained retrieval output image and the query image. The result demonstrates that the proposed CBIR approach is robust and effectual compared with other latest work.