Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: An assessment

Zheng, Bin; Sumkin, Jules H.; Good, Walter F.; Maitz, Glenn S.; Chang, Yuan‐Hsiang; Gur, David

doi:10.1016/s1076-6332(00)80574-7

Cited by 48 publications

(29 citation statements)

References 24 publications

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“…Furthermore, metrics of different computational principles, such as edge-based-measures, spectral distance-based measures, and metrics simulating human visual system [29] should also be tested. Finally, future efforts will focus on investigating the effect of image compression on the performance of computer-aided detection [38] and diagnosis schemes, accounting for an indirect approach towards quantitative image quality evaluation of compressed mammograms, currently being under-researched.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

et al. 2012

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“…The detailed definitions and computing methods for these 14 features have been previously reported [14]. A feature-based artificial neural network (ANN) is then applied to generate an initial detection score (the likelihood of the suspected region depicting an actual mass) [23]. The topographic region growth algorithm had been implemented and used in our previous CAD scheme [8].…”

Section: A Automated Detection and Classification Of Mass Region Spimentioning

confidence: 99%

“…The topographic region growth algorithm had been implemented and used in our previous CAD scheme [8]. It plays an important role in reducing false-positive detections in that it typically eliminates approximately 75%-80% of suspected lesions (i.e., from an average of over 15 to approximately 3 regions per image) identified by the Difference-of-Gaussian filtering method employed to identify all possible regions that may depict masses; thereby, sensitivity remains high (i.e., > 85% of image based sensitivity or > 95% case based sensitivity) [23]. In order to minimize the risk of over segmentation (penetration of the growth region into surrounding normal breast tissue), current growth threshold in each layer is conservatively controlled by local contrast measurement [22].…”

Section: A Automated Detection and Classification Of Mass Region Spimentioning

confidence: 99%

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

et al. 2007

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Rationale and Objectives: The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions. Materials and Methods:A large and diverse reference library involving 3000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion. Results:In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (p = 0.128). Conclusion:This study suggests that using the new automated ICAD scheme, the inter-observer variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (p < 0.05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

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“…Several lossless and lossy compression method have been presented to resolve this problem [7][8][9][10][11][12][13][14][15][16][17][18]. However, lossless compression has brought about at most only 4:1 compression ratio.…”

Section: Introductionmentioning

confidence: 99%

“…However, lossless compression has brought about at most only 4:1 compression ratio. Most lossy compression algorithms need take into account the special clinical issue to be dealt with [8][9][10][11][12][13][14][15][16][17]. Receiver operation characteristic(ROC) analysis on lossy compression show that it is promising to use lossy techniques in medical image compression [8].…”

Section: Introductionmentioning

confidence: 99%

A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework

Xu¹,

Zuo²,

Xu³

et al. 2011

IJMECS

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Abstract-With the rapidly growing use of digital images in medical archival and communication, image compression technology, especially diagnosis lossless compression technology, plays a more and more important role for medical applications. In this thesis, a novel diagnosis loseless compression algorithm is presented for digital mammography. The mammogram is divided into breast region, pectoral muscle and background using the CAD technology. Then mutiple arbitrary shape ROIs coding framework is used to compress the mammogram in which the breast region and pectoral muscle are compressed losslessly and lossily respectively, and the background can be discarded or compressed lossily as user's will. Experimental results show that the proposed method offer potential advantage in medical applications of digital mammography compression.

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Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: An assessment

Cited by 48 publications

References 24 publications

Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

A New Diagnosis Loseless Compression Method for Digital Mammography Based on Multiple Arbitrary Shape ROIs Coding Framework

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