STATISTICAL SURFACE FRACTAL ANALYZER OF BREAST CANCER

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$99,822.00
Award Year:
2001
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Agency Tracking Number:
1R43CA085101-01A1
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
ALAN PENN AND ASSOCIATES, INC.
14 CLEMSON CT, ROCKVILLE, MD, 20850
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
ALAN PENN
() -
Business Contact:
(301) 279-5958
APENN@ALANPENN.COM
Research Institution:
n/a
Abstract
DESCRIPTION (Verbatim from the Applicant's Abstract): The applicants will develop statistical SURFACE fractal dimension (S-fd) features, which will discriminate benign from malignant breast masses on MRI and mammographic images. S-fd features derived from three functional representations of breast mass image data will be evaluated: (1) signal intensity of mass on single MRI slice; (2) mammographic density of mass on digitized mammogram; (3) thickness of mass, computed from 3-dim MRI data. The S-fd features are statistics from Fractal Interpolation Function Models (FIFM) of breast mass image data. In prior research, FIFM BORDER fd (B-fd) features were shown to provide more robust discrimination in data-limited applications such as breast mass analysis than other fd algorithms. FIFM SURFACE fractals represent multiresolution differences between benign and malignant masses more accurately and more extensively than FIFM BORDER fractals, and therefore may provide more reliable discriminatory information. Robust S-fd features, which discriminate benign from malignant masses, will have application in computer-aided-diagnosis systems under development. PROPOSED COMMERCIAL APPLICATION: The new features will have significant value to the diagnostician who must distinguish benign from malignant breast lesions. The algorithm is readily integrated into CAD systems and has potential utility for a variety of medical and industrial applications in texture analysis of data-limited surfaces.

* information listed above is at the time of submission.

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