TECHNOLOGY SUMMARY
Barcodes for Image Search Engine
Background
Medical imaging software is estimated to be a $2 billion USD market and
projected to spike to $35 billion USD by 2019. This growth is linked to the
explosion in imaging data and the need for big data processing and
analytics delivered over cloud computing platforms. Within the radiology
field, disease misdiagnosis is a major problem that not only leads to
increased healthcare costs but also tragically to the premature loss of life.
There are estimated to be 500,000 cases of misdiagnosis in the US alone
which quite often leads to costly malpractice lawsuits and by extension
cost pressure on health insurance premiums for everyone. Many of these
errors could be avoided by expanding the radiologists access to a larger
database of existing patient images associated with confirmed diagnosis to
compare to current patient images and thus enhance the decision making
process.
Description of the invention
Reference
8810-7411
Patent status
US Provisional Patent filed
Stage of development
Matlab prototype developed
Validated against state-of-the-art
using a public benchmark dataset
with 15,000 images
Seeking industrial partner for
medical imaging processing
Studies for additional markets are
on-going
Contact
Scott Inwood
Director of Commercialization
Waterloo Commercialization Office
519-888-4567, ext. 33728
[email protected]
uwaterloo.ca/research
Waterloo has developed a method that uses specialized algorithms (based
on Radon transforms) to convert images, particularly the region of interest
(ROI) in an image (eg. tumour), into a unique barcode representation.
These barcode representations can then be used to annotate images with
searchable ROI information (ie. what a confirmed tumour looks like from
existing data). This approach is particularly useful for medical imaging
whereby any lesion and suspicious mass can be “barcoded” (see figures
on the left panel). As these barcodes are short compact binary codes (0’s
and 1’s) they are inherently in a format enabling the fastest level of
computational speed which makes them ideal for constructing a highly
efficient image search engine.
By annotating large publically available image databases (ie. PACS and
RIS) with ROI barcodes, a searchable image database can be created that
can be used by radiologists to compare with newly generated patient
images. This approach enables radiologists to quickly to get a “virtual
second opinion” of their own diagnostic impression and thus reduce the
chances of a misdiagnosis.
Advantages
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Barcodes are compact and thus don’t’ require much memory storage
Binary information makes search very fast
Both global (similar images) and local (similar image parts) search
possible
Radon barcodes can be reversible using customized software to
reproduce the original image
Can be extended to video applications
Barcode representations do not represent a privacy problem
Potential applications
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Diagnostic Radiology
Pathology
Satellite Imaging
Internet/Personal Image Search