Picture Quality According to the Type of Detector in Full-field Digital Mammography

Kim, Hyunsoo; Jung, Jaeho; Lee, Jong-Woong; Kang, Hee-Doo; Dong, Ke; Chung, Woon-Kwan; Kim, Eun-Soo

doi:10.3938/jkps.58.364

Cited by 4 publications

(7 citation statements)

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“…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%

“…Pixel difference metrics [20,30,31] for calculating the distortion between the compressed and the original image in a pixel-wise basis: MSE, MAE, normalized absolute error (NAE), SNR, and PSNR. II.Correlation-based metrics for quantifying the similarity between two digital images in terms of the correlation function [30]: structural content (SC) and normalized cross-correlation (NK).…”

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%

“…Zyout et al [19] employed mean square error (MSE) and PSNR metrics for evaluating a wavelet-based progressive lossy to lossless image compression scheme applied on regions of interest (ROIs) of digitized mammograms containing MCs. Kim et al [20] exploited objective metrics such as SNR, PSNR, root mean square error, and mean average error (MAE) for evaluating image quality of digital mammograms compressed by JPEG2000 and JPEG, in case of direct and indirect detectors in FFDM. Przelakowski et al [21] proposed a hybrid image quality measure reflecting diagnostic accuracy, based on a linear combination of image quality metrics.…”

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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Section: Quantitative Image Quality Evaluationmentioning

confidence: 99%

Section: Quantitative Image Quality Evaluationmentioning

confidence: 99%

Section: Quantitative Image Quality Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Enhancement Method of Depth Accuracy in DIBR-Based Multiview Image Generation

Kim¹,

Cho²,

Park³

2016

KIPS Transactions on Computer and Communication Systems

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DIBR (Depth Image Based Rendering) is a multimedia technology that generates the virtual multi-view images using a color image and a depth image, and it is used for creating glasses-less 3-dimensional display contents. This research describes the effect of depth accuracy about the objective quality of DIBR-based multi-view images. It first evaluated the minimum depth quantization bit that enables the minimum distortion so that people cannot recognize the quality degradation. It then presented the comparative analysis of non-uniform domain-division quantization versus regular linear quantization to find out how effectively express the accuracy of the depth information in same quantization levels according to scene properties.

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