An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

Abstract

In past decade, the use of medical imaging for disease diagnosis is increased rapidly. The medical images provide useful information about the anatomy of patients. The medical images are used not only to assist the doctors for diagnosis purpose, but also used in Research & Development for deeper insights and better understanding into cause and cure of numerous diseases. To retrieve medical images from large scale repositories in real time, there is urgent need of an efficient medical image retrieval system. For this purpose, an efficient clustering based content based image retrieval (CBIR) system using Hadoop is proposed to analyze massive magnetic resonance imaging (MRI) images dataset for early disease diagnosis. The proposed CBIR system uses Hadoop platform, local mesh peak valley edge patterns (LMePVEP) for feature extraction, MapReduce based parallel k-means algorithm for clustering and euclidean distance to measure similarity. The method proposed is tested and compared with state-of-the-art CBIR methods on massive MRI images dataset. The experimental results obtained show that the method proposed in our work outperforms traditional CBIR methods in terms of average retrieval time and mean average precision for massive MRI images dataset.