A Perceptual Matching based Deduplication Scheme using Gabor-ORB Filters for Medical Images

In the ever widening field of telemedicine, there is a greater need for intelligent methods to selectively choose data that are relevant enough to be transmitted over a network and checked remotely. By the very nature of medical imaging, a large amount of data is generated per imaging or scanning session. For instance, a Magnetic Resonance Images (MRI) scan consist of hundreds to thousands of images related to slices of the organ being scanned. But at often times all of these slices are not of interest during the process of medical diagnosis by the medical practitioner. Not only does this result in the access of unwanted data remotely, but it can also put greater strain on the bandwidth available over the network. If the relevant images can be selected automatically without human intervention, ensuring great sensitivity, the above-mentioned issues can also be alleviated. This paper proposes a novel method of perceptual matching and selection of relevant MRI images by using a deduplicating technique of combining Gabor filter with Oriented FAST and Rotated BRIEF (ORB) feature extraction technique on a vast set of MRI scan images. The outcome of this method are relevant deduplicated MRI scan images which can save the bandwidth and will be easy for the medical practitioner to verify remotely.