Map-Reduce based framework for instrument detection in large-scale surgical videos

Modern digital world produces massive amount of data generally refereed as Big Data, which play important roles in dictating the quality of our lives. Relationships among such data have high value, but extremely complex task to establish. Medical Field is one of the major big data sources which produces big volume of data. Modern surgical tools have the capability to record High Definition(HD) videos during the surgical procedure which enables post surgical reviews. Such tools produces giga bytes(GB) of video footage after every surgery which needs mass storage and complex processing. A major solution for this problem is parallel distributed processing using Hadoop based Map Reduce Framework. This paper proposes a Surgical Video Analysis Framework using Hadoop to analyze large surgical videos, for identifying surgical instruments used. Framework first converts videos into large number of frames and using Hadoop Image Process Interface (HIPI) it is converted to HIB image bundles. Parallel processing of images in the bundle is done by mappers and identified instrument frame information's are logged. Three different feature extraction methods: Scale-Invariant Feature Transform(SIFT), Speeded Up Robust Features(SURF) with Support Vector Machines(SVM) and Haralick Texture Descriptor with Support Vector Machines(SVM) is used in mappers for local image processing.