Image quality in lossy compressed digital mammograms

Perlmutter, S.M.; Cosman, Pamela C.; Gray, Robert M.; Olshen, Richard A.; Ikeda, Debra M.; Adams, Chris; Betts, Bradley J.; Williams, Mark B.; Perlmutter, K.O.; Li, J.; Aiyer, A.; Fajardo, Laurie L.; Birdwell, Robyn L.; Daniel, Bruce L.

doi:10.1016/s0165-1684(97)00046-7

Cited by 40 publications

(27 citation statements)

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“…The quantitative image quality approach of compressed mammograms has been considered as an additional option to observer performance analysis [15,18], as well as in the framework of evaluating image compression schemes utilizing mammographic images [19][20][21], phantom images [34], and computer-generated test objects images [10]. However, the quantitative image quality approach has not been exploited towards the determination of visually or diagnostically lossless CR thresholds in mammography.…”

Section: Discussionmentioning

confidence: 99%

“…In case of mammographic image compression, metrics such as: SNR, PSNR, MAE, and MSE have been previously exploited [15,[18][19][20], while a linear combination of metrics has been proposed and correlated to diagnostically related image quality [21].…”

Section: Quantitative Image Quality Evaluationmentioning

confidence: 99%

“…SNR SNR [18,20] is defined as the power ratio between a signal (meaningful information) and background noise (unwanted signal). Because many signals have a very wide dynamic range, it is often expressed using the logarithmic decibel scale.…”

Section: Quantitative Image Quality Evaluationmentioning

confidence: 99%

“…Quantitative image quality approaches of compressed mammograms have been considered in the framework of evaluating image compression schemes [18][19][20][21] but have not been exploited toward the determination of visually or diagnostically lossless CR thresholds. Sung et al [15] considered a quantitative approach toward image quality evaluation by means of the peak signal-to-noise ratio (PSNR) metric in addition to observer studies performed for lossy CR threshold determination.…”

Section: Introductionmentioning

confidence: 99%

“…Sung et al [15] considered a quantitative approach toward image quality evaluation by means of the peak signal-to-noise ratio (PSNR) metric in addition to observer studies performed for lossy CR threshold determination. Perlmutter et al [18] employed the signal-to-noise ratio (SNR) metric and observer ratings reflecting mammographic interpretation criteria (related to screening and diagnosis) to assess image quality of lossy compressed mammograms as compared with analog or digital originals. Results demonstrated that digital mammograms compressed in a lossy fashion to 0.015 bits per pixel (bpp), about 80:1 CR, using an embedded wavelet coding scheme result in no significant differences from the analog or digital originals.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

et al. 2012

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The current study presents a quantitative approach towards visually lossless compression ratio (CR) threshold determination of JPEG2000 in digitized mammograms. This is achieved by identifying quantitative image quality metrics that reflect radiologists' visual perception in distinguishing between original and wavelet-compressed mammographic regions of interest containing microcalcification clusters (MCs) and normal parenchyma, originating from 68 images from the Digital Database for Screening Mammography. Specifically, image quality of waveletcompressed mammograms (CRs, 10:1, 25:1, 40:1, 70:1, 100:1) is evaluated quantitatively by means of eight image quality metrics of different computational principles and qualitatively by three radiologists employing a five-point rating scale. The accuracy of the objective metrics is investigated in terms of (1) their correlation (r) with qualitative assessment and (2) ROC analysis (A z index), employing pooled radiologists' rating scores as ground truth. The quantitative metrics mean square error, mean absolute error, peak signal-to-noise ratio, and structural similarity demonstrated strong correlation with pooled radiologists' ratings (r, 0.825, 0.823, −0.825, and −0.826, respectively) and the highest area under ROC curve (A z , 0.922, 0.920, 0.922, and 0.922, respectively). For each quantitative metric, the highest accuracy values of corresponding ROC curves were used to define metric cut-off values. The metrics cut-off values were subsequently used to suggest a visually lossless CR threshold, estimated to be between 25:1 and 40:1 for the dataset analyzed. Results indicate the potential of the quantitative metrics approach in predicting visually lossless CRs in case of MCs in mammography.

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