Detection of simulated lesions on data-compressed digital mammograms

Sankararaman Suryanarayanan, Andrew Karellas, Srinivasan Vedantham, Sandra M. Waldrop, Carl J. D'Orsi

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

PURPOSE: To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms. MATERIALS AND METHODS: The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (Az) across the three compression levels for the masses and microcalcification clusters. RESULTS: The results of the study indicated no significant differences in the Az values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in Az values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study. CONCLUSION: Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.

Original languageEnglish (US)
Pages (from-to)31-36
Number of pages6
JournalRadiology
Volume236
Issue number1
DOIs
StatePublished - Jul 1 2005
Externally publishedYes

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Calcinosis
Data Compression
ROC Curve
Social Adjustment
Research Ethics Committees
Social Responsibility
Health Insurance
Informed Consent
Analysis of Variance

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Suryanarayanan, S., Karellas, A., Vedantham, S., Waldrop, S. M., & D'Orsi, C. J. (2005). Detection of simulated lesions on data-compressed digital mammograms. Radiology, 236(1), 31-36. https://doi.org/10.1148/radiol.2361040741

Detection of simulated lesions on data-compressed digital mammograms. / Suryanarayanan, Sankararaman; Karellas, Andrew; Vedantham, Srinivasan; Waldrop, Sandra M.; D'Orsi, Carl J.

In: Radiology, Vol. 236, No. 1, 01.07.2005, p. 31-36.

Research output: Contribution to journalArticle

Suryanarayanan, S, Karellas, A, Vedantham, S, Waldrop, SM & D'Orsi, CJ 2005, 'Detection of simulated lesions on data-compressed digital mammograms', Radiology, vol. 236, no. 1, pp. 31-36. https://doi.org/10.1148/radiol.2361040741
Suryanarayanan, Sankararaman ; Karellas, Andrew ; Vedantham, Srinivasan ; Waldrop, Sandra M. ; D'Orsi, Carl J. / Detection of simulated lesions on data-compressed digital mammograms. In: Radiology. 2005 ; Vol. 236, No. 1. pp. 31-36.
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abstract = "PURPOSE: To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms. MATERIALS AND METHODS: The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (Az) across the three compression levels for the masses and microcalcification clusters. RESULTS: The results of the study indicated no significant differences in the Az values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in Az values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study. CONCLUSION: Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.",
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AB - PURPOSE: To evaluate retrospectively the effect of a wavelet-based compression method on the detection of simulated masses of various sizes and clustered microcalcifications on data-compressed digital mammograms. MATERIALS AND METHODS: The images used in this study were acquired with institutional review board approval and patient informed consent, both of which allowed subsequent image data analysis. Patient identification was removed from images, and the study complied with requirements of the Health Insurance Portability and Accountability Act. Masses 3, 6, and 8 mm in diameter were analytically simulated and added to clinical mammographic backgrounds. In addition, microcalcifications were extracted from a clinical mammogram and hybridized with simulated microcalcifications for use in this study. Image compression conditions of 1:1, 15:1, and 30:1 were investigated. Observer responses were recorded with a six-point rating scale, and receiver operating characteristic (ROC) analysis was performed. In addition, two well-established numeric observer models were used to study the effect of image compression under the same compression conditions as were used with human observers. Analysis of variance was performed after observer adjustment to compare the mean values for area under the ROC curve (Az) across the three compression levels for the masses and microcalcification clusters. RESULTS: The results of the study indicated no significant differences in the Az values for masses with the compression conditions investigated. For images of microcalcifications, there were significant differences in Az values between compression ratios of 1:1 and 30:1 (P = .0005) and of 15:1 and 30:1 (P = .004); the difference between compression ratios of 1:1 and 15:1 was nonsignificant (P = .053). The observer models and human observers exhibited similar trends in detection of the masses investigated in this study. CONCLUSION: Detection of simulated masses was not affected by the compression method with the conditions used in this study, while the detection of microcalcifications was significantly reduced with a compression ratio of more than 15:1.

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