Why Image Analytics Holds the Key to Better Big Data Analysis

In the 2012 Hollywood movie “Act of Valor”, US Navy Seals launch a Raven UAV to procure live video streaming for identifying targets prior to a raid. In yet another movie, “Zero Dark Thirty”, analysts use FMV/ drone feed to re-create Bin Laden’s compound for training US Navy Seals, and later guide them to accomplish their mission. The growing use of image analytics in tracking, detecting, analyzing and predicting outcomes have been effectively used by story writers to piece amazing stories.  This blog analyzes the role of image analytics in enhancing the preciseness of predictive technology like big data.

Image analytics is a technique by which an image is digitally processed for extracting and analyzing data for insightful information. Big data still remains a scary and invincible concept, because of the unmanageable amount of unstructured data present in it. Images and videos constitute 80 percent of this unstructured big data, and given its leaping pervasiveness, time has come for analytical systems to integrate and interpret images and videos as well as they interpret structured data.

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Let’s take the example of fingerprint matching to understand how image analytics can standardize data. Fingers and their images come in different size and quality, thus making them wholly unstructured and hence, difficult to match. To structure it, agencies first identify a set of critical points on each print and use it to create a standard polygon. The polygon is fully structured and is created from a completely unstructured image. The actual analysis is now carried out with the help of these structured polygons. In a similar fashion, image analytics can be used to standardize the unstructured data which comes in the form of images and video.

A recent news item on cancer detection shows how healthcare can serve as a perfect domain for exploring the potential of image analytics. In the US, until about 15 years back, the majority of breast cancer patients didn’t require chemotherapy treatment, yet all were made to undergo it because doctors were unable to distinguish aggressive cancer from non-aggressive ones. This led to serious side effects in patients afflicted with mild cancer. Soon, a genomic test was developed, in which a biopsy sample is sent to a company for analysis and assignment of a risk score, based on which doctors determine the treatment dosage. The test, however, has severe drawbacks — it requires shipping, destroys tissue, and is expensive.

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Today, the need for this test stands obviated, as analysts can mine radiologic data from MRIs to discover differences in gene expression of afflicted patients and determine quantitatively which patients need chemotherapy. The benefits: no shipping, no waiting period, and cost-effective treatment. Prior to this, researchers from the University of Michigan used computed image analytics of CT scans to identify actual cases of COPD, an obstructive lung disease which leads to serious shortness of breath.;

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