Fast wavelet based image characterization for content based medical image retrieval

A large collection of medical images surrounds health care centers and hospitals. Medical images produced by different modalities like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and X-rays have increased incredibly with the advent of latest technologies for image acquisition. Retrieving clinical images of interest from these large data sets is a thought-provoking and demanding task. In this paper, a fast wavelet based medical image retrieval system is proposed that can aid physicians in the identification or analysis of medical images. The image signature is calculated using kurtosis and standard deviation as features. A possible use case is when the radiologist has some suspicion on diagnosis and wants further case histories, the acquired clinical images are sent (e.g. MRI images of brain) as a query to the content based medical image retrieval system. The system is tuned to retrieve the top most relevant images to the query. The proposed system is computationally efficient and more accurate in terms of the quality of retrieved images.