Vector Quantization for Image Pattern Recognition

Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: 1R43EB001617-01
Agency Tracking Number: EB001617
Amount: $97,269.00
Phase: Phase I
Program: SBIR
Awards Year: 2003
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
APERIO TECHNOLOGIES, INC., 1430 VANTAGE CT, STE 106, VISTA, CA, 92083
DUNS: N/A
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 DIRK SOENKSEN
 (760) 539-1101
 DSOENKSEN@APERIO.COM
Business Contact
 DIRK SOENKSEN
Phone: (760) 539-1101
Email: DSOENKSEN@APERIO.COM
Research Institution
N/A
Abstract
DESCRIPTION (provided by applicant): This Phase-I SBIR application addresses the increasingly significant challenges faced by pathologists and clinicians in manually inspecting microscope slides. Microscopic inspection suffers from being labor-intensive, subjective, expensive and limited by the need for physical access to the glass slide specimen of interest. The obstacle to automated microscopic inspection has been the inability to efficiently digitize entire microscope specimens at high resolutions. Aperio has developed the ScanScope (R), a novel microscope slide scanner that makes it practical - for the first time - to rapidly create virtual microscope slides at high resolutions. Virtual slides set the stage for automating microscopic inspection using automated pattern recognition. This research aims to adapt and optimize Aperio's existing and novel algorithms for vector quantization (VQ) to the problem of automatic pattern recognition in virtual slides. VQ is a general mathematical technique for encoding bitstreams using a vocabulary. The primary aim is to demonstrate the feasibility of using VQ for pattern recognition in a practical and well-characterized application: automatically finding virtually all micrometastasis clusters in cytology specimens. This proposed research represents a first attempt to automate pattern recognition in virtual slides using VQ.

* Information listed above is at the time of submission. *

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