Method for determining an optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms
US6075878A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Nov 28, 1997 |
| Grant date | Jun 13, 2000 |
| Priority date | — |
| Expiry date | Nov 28, 2017 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T7/0012
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-aided diagnosis (CAD) method for detection of clustered microcalcifications in digital mammograms based on an image reconstruction using a substantially optimally weighted wavelet transform. Weights at individual scales of the wavelet transform are optimized based on a supervised learning method. In the learning method, an error function represents a difference between a desired output and a reconstructed image obtained from weighted wavelet coefficients of the wavelet transform for a given mammogram. The error function is then minimized by modifying the weights by means of a conjugate gradient algorithm. Performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis which indicated that the present invention outperformed both a difference-image technique and partial reconstruction method currently used in CAD methods.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.