RATIONALE AND OBJECTIVES: We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS: A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS: The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION: The wavelet transform approach can improve the detection of subtle clustered microcalcifications.
RATIONALE AND OBJECTIVES: We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS: A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS: The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION: The wavelet transform approach can improve the detection of subtle clustered microcalcifications.
Authors: S Vedantham; A Karellas; S Suryanarayanan; D Albagli; S Han; E J Tkaczyk; C E Landberg; B Opsahl-Ong; P R Granfors; I Levis; C J D'Orsi; R E Hendrick Journal: Med Phys Date: 2000-03 Impact factor: 4.071
Authors: S Vedantham; A Karellas; S Suryanarayanan; I Levis; M Sayag; R Kleehammer; R Heidsieck; C J D'Orsi Journal: Med Phys Date: 2000-08 Impact factor: 4.071
Authors: Roberto R Pereira; Paulo M Azevedo Marques; Marcelo O Honda; Sergio K Kinoshita; Roger Engelmann; Chisako Muramatsu; Kunio Doi Journal: J Digit Imaging Date: 2007-09 Impact factor: 4.056