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

A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

PURPOSE

Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses.

METHODS

An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method.

RESULTS

Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized using other three feature selection methods. In addition, among 271 features, the shape, local morphological features, fat and calcification based features were the most frequently selected features to build ANNs.

CONCLUSIONS

Although conventional GA is a powerful tool in optimizing classifiers used in CAD schemes of medical images, it is very computationally intensive. This study demonstrated that using a new SFFS based approach enabled to significantly improve efficacy of image feature selection for developing CAD schemes.

Quantitative visually lossless compression ratio determination of JPEG2000 in digitized mammograms

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 wavelet-compressed 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.

Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble classifier

RATIONALE AND OBJECTIVES

Accurate classification is critical in mammography computer-aided diagnosis using content-based image retrieval approaches (CBIR CAD). The objectives of this study were to: 1) develop an accurate ensemble classifier based on domain knowledge and a robust feature selection method for CBIR CAD; 2) propose three new features; and 3) assess the performance of the proposed method and new features by using a relatively large imaging data set.

MATERIALS AND METHODS

The data set used in this study consisted of 2114 regions of interest (ROI) extracted from a publicly available image database. The proposed ensemble classifier method we called E-DGA-KNN included four steps. In the first step, 804 ROIs depict masses were divided into five classes according to their boundary types. Then, each class of ROI with an equal number of negative ROIs were put together to create a sub-database. Second, a dual-stage genetic algorithm, which was called DGA, was applied on those five sub-databases for feature selection and weights determination respectively. In the third step, five base K-nearest neighbor (KNN) classifiers were created by using the results of the second step on 2114 ROIs, and five detection scores for a given queried ROI were obtained. Finally, these classifiers are combined to yield a final classification. The performances of the proposed methods were evaluated by using receiver operating characteristic (ROC) analysis. A comparison with eight different methods on the data set was provided which include the stepwise linear discriminative analysis algorithm (SLDA) and particle swarm optimization (PSO) algorithm with KNN classifier.

RESULTS

When four hybrid feature selection methods were applied with single KNN classifier (ie, DGA-KNN, SLDA-WGA-KNN, SLDA-PSO-KNN, GA-PSO-KNN) and the proposed E-DGA-KNN method to the data set, the computed areas under the ROC curve (Az) were 0.8782 ± 0.0080, 0.8675 ± 0.0081, 0.8623 ± 0.0083, 0.8725 ± 0.0079, and 0.8927 ± 0.0073, respectively. If all features and single KNN classifier were used, the Az value was 0.8478 ± 0.0088. Az values were 0.8592 ± 0.0083 and 0.8632 ± 0.0081 when SLDA or GA algorithm used alone.

CONCLUSIONS

In this study, an ensemble classifier based on domain knowledge and a dual-stage feature selection method was proposed. Evaluation results indicated that the proposed method achieved largest value of ROC compared to other algorithms. The proposed method shows better performance and has the potential to improve the performance of CBIR CAD in interpreting and analyzing mammograms.

Acceptable compression ratio of full-field digital mammography using JPEG 2000

AIM

To estimate the acceptable compression ratio of full-field digital mammography (FFDM) using the Joint Photographic Experts Group (JPEG) 2000 compression algorithm.

MATERIALS AND METHODS

Eighty cases that included images of 40 masses (20 benign, 20 malignant) and 40 microcalcifications (20 benign, 20 malignant) were collected. The images were compressed to five different lossy ratios: 20:1, 40:1, 60:1, 80:1, and 100:1, and four radiologists independently determined whether the compressed group was distinguishable from the control group. The ratio of the compressed group that was rated indistinguishable from the control group was compared for each reviewer, and the results were analysed for agreements of three or more reviewers.

RESULTS

The ability to distinguish the compressed image from the control group is given as a range across the four reviewers: 0-1.3% (0/80 to 1/80) of the 20:1, 0-2.5% (0/80 to 2/80) of the 40:1, 5-7.5% (4/80 to 6/80) of the 60:1, 10-37.5% (8/80 to 30/80) of the 80:1, and 30-87.5% (24/80 to 70/80) of the 100:1. For three compression groups (20:1, 40:1, and 60:1), three or more reviewers agreed that there was a distinguishable difference for 0/80, 0/80, and 3/80 images, respectively. Thus, the compressed images do not differ significantly from the control group (p>0.05). However, the 80:1 and 100:1 compressed images were different for 9/80 and 29/80 images, respectively, which is significantly different from the control group (p<0.05).

CONCLUSION

The lossy 60:1 compression ratio for FFDM is visually identical to the control image and, therefore, potentially acceptable for primary interpretation.

Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment

OBJECTIVES

To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database.

METHODS

The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database.

RESULTS

The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups.

CONCLUSION

This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.

Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives

As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.

Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers

RATIONALE AND OBJECTIVES

Global data-based and local instance-based machine-learning methods and classifiers have been widely used to optimize computer-aided detection and diagnosis (CAD) schemes to classify between true-positive and false-positive detections. In this study, the correlation between these two types of classifiers was investigated using a new independent testing data set, and the potential improvement of a CAD scheme's performance by combining the results of the two classifiers in detecting breast masses was assessed.

MATERIALS AND METHODS

The CAD scheme first used image filtering and a multilayer topographic region growth algorithm to detect and segment suspicious mass regions. The scheme then used an image feature-based classifier to classify these regions into true-positive and false-positive regions. Two classifiers were used in this study. One was a global data-based machine-learning classifier, an artificial neural network (ANN), and the other was a local instance-based machine-learning classifier, a k-nearest neighbor (KNN) algorithm. An independent image database including 400 mammographic examinations was used in this study. Of these, 200 were cancer cases and 200 were negative cases. The preoptimized CAD scheme was applied twice to the database using the two different classifiers. The correlation between the two sets of classification results was analyzed. Three sets of CAD performance results using the ANN, KNN, and average detection scores from both classifiers were assessed and compared using the free-response receiver-operating characteristic method.

RESULTS

The results showed that the ANN achieved higher performance than the KNN algorithm, with a normalized area under the performance curve (AUC) of 0.891 versus 0.845. The correlation coefficients between the detection scores generated by the two classifiers were 0.436 and 0.161 for the true-positive and false-positive detections, respectively. The average detection scores of the two classifiers improved CAD performance and reliability by increasing the AUC to 0.912 and reducing the standard error of the estimated AUC by 14.4%. The detection sensitivity was also increased from 75.8% (ANN) and 65.9% (KNN) to 80.3% at a false-positive detection rate of 0.3 per image.

CONCLUSIONS

This study demonstrates that two global data-based and local data-based machine-learning classifiers (ANN and KNN) generated low correlated detection results and that combining the detection scores of these two classifiers significantly improved overall CAD performance (P < .01) and reduced standard error in CAD performance assessment.

Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment

This study aims to assess three methods commonly used in content-based image retrieval (CBIR) schemes and investigate the approaches to improve scheme performance. A reference database involving 3000 regions of interest (ROIs) was established. Among them, 400 ROIs were randomly selected to form a testing dataset. Three methods, namely mutual information, Pearson's correlation and a multi-feature-based k-nearest neighbor (KNN) algorithm, were applied to search for the 15 'the most similar' reference ROIs to each testing ROI. The clinical relevance and visual similarity of searching results were evaluated using the areas under receiver operating characteristic (ROC) curves (A(Z)) and average mean square difference (MSD) of the mass boundary spiculation level ratings between testing and selected ROIs, respectively. The results showed that the A(Z) values were 0.893 +/- 0.009, 0.606 +/- 0.021 and 0.699 +/- 0.026 for the use of KNN, mutual information and Pearson's correlation, respectively. The A(Z) values increased to 0.724 +/- 0.017 and 0.787 +/- 0.016 for mutual information and Pearson's correlation when using ROIs with the size adaptively adjusted based on actual mass size. The corresponding MSD values were 2.107 +/- 0.718, 2.301 +/- 0.733 and 2.298 +/- 0.743. The study demonstrates that due to the diversity of medical images, CBIR schemes using multiple image features and mass size-based ROIs can achieve significantly improved performance.

Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library

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 3,000 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 = .128).

CONCLUSIONS

This study suggests that using the new automated ICAD scheme, the interobserver 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 < .05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

Multiview-based computer-aided detection scheme for breast masses

In this study, we developed and tested a new multiview-based computer-aided detection (CAD) scheme that aims to maintain the same case-based sensitivity level as a single-image-based scheme while substantially increasing the number of masses being detected on both ipsilateral views. An image database of 450 four-view examinations (1800 images) was assembled. In this database, 250 cases depicted malignant masses, of which 236 masses were visible on both views and 14 masses were visible only on one view. First, we detected suspected mass regions depicted on each image in the database using a single-image-based CAD. For each identified region (with detection score > or = 0.55), we then identified a matching strip of interest on the ipsilateral view based on the projected distance to the nipple along the centerline. By lowering CAD operating threshold inside the matching strip, we searched for a region located inside the strip and paired it with the original region. A multifeature-based artificial neural network scored the likelihood of the paired "matched" regions representing true-positive masses. All single (unmatched) regions except for those either with very high detection scores (> or = 0.85) or those located near the chest wall that cannot be matched on the other view were discarded. The original single-image-based CAD scheme detected 186 masses (74.4% case-based sensitivity) and 593 false-positive regions. Of the 186 identified masses, 91 were detected on two views (48.9%) and 95 were detected only on one view (51.1%). Of the false-positive detections, 54 were paired on the ipsilateral view inside the corresponding matching strips and the remaining 485 were not, which represented 539 case-based false-positive detections (0.3 per image). Applying the multiview-based CAD scheme, the same case-based sensitivity was maintained while cueing 169 of 186 masses (90.9%) on both views and at the same time reducing the case-based false-positive detection rate by 23.7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate.

A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment

The purpose of this study was to develop and test a method for selecting "visually similar" regions of interest depicting breast masses from a reference library to be used in an interactive computer-aided diagnosis (CAD) environment. A reference library including 1000 malignant mass regions and 2000 benign and CAD-generated false-positive regions was established. When a suspicious mass region is identified, the scheme segments the region and searches for similar regions from the reference library using a multifeature based k-nearest neighbor (KNN) algorithm. To improve selection of reference images, we added an interactive step. All actual masses in the reference library were subjectively rated on a scale from 1 to 9 as to their "visual margins speculations". When an observer identifies a suspected mass region during a case interpretation he/she first rates the margins and the computerized search is then limited only to regions rated as having similar levels of spiculation (within +/-1 scale difference). In an observer preference study including 85 test regions, two sets of the six "similar" reference regions selected by the KNN with and without the interactive step were displayed side by side with each test region. Four radiologists and five nonclinician observers selected the more appropriate ("similar") reference set in a two alternative forced choice preference experiment. All four radiologists and five nonclinician observers preferred the sets of regions selected by the interactive method with an average frequency of 76.8% and 74.6%, respectively. The overall preference for the interactive method was highly significant (p < 0.001). The study demonstrated that a simple interactive approach that includes subjectively perceived ratings of one feature alone namely, a rating of margin "spiculation," could substantially improve the selection of "visually similar" reference images.

Free-response receiver operating characteristic evaluation of lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms

PURPOSE

To assess the effects of two irreversible wavelet-based compression algorithms--Joint Photographic Experts Group (JPEG) 2000 and object-based set partitioning in hierarchical trees (SPIHT)--on the detection of clusters of microcalcifications and masses on digitized mammograms.

MATERIALS AND METHODS

The use of the images in this retrospective image-collection study was approved by the institutional review board, and patient informed consent was not required. One hundred twelve mammographic images (28 with one or two clusters of microcalcifications, 19 with one mass, 17 with both abnormal findings, and 48 with normal findings) obtained in 60 women who ranged in age from 25 to 79 years were digitized and compressed at 40:1 and 80:1 by using the JPEG2000 and object-based SPIHT methods. Five experienced radiologists were asked to locate and rate clusters of microcalcifications and masses on the original and compressed images in a free-response receiver operating characteristic (FROC) data acquisition paradigm. Observer performance was evaluated with the jackknife FROC method.

RESULTS

The mean FROC figures of merit for detecting clusters of microcalcifications, masses, and both radiographic findings on uncompressed images were 0.80, 0.81, and 0.72, respectively. With object-based SPIHT 80:1 compression, the corresponding values were larger than the values for uncompressed images by 0.005, 0.009, and -0.005, respectively. The 95% confidence interval for the differences in figures of merit between compressed and uncompressed images was -0.039, 0.033 for the microcalcification finding; -0.055, 0.034 for the mass finding; and -0.039, 0.030 for both findings. Because each of these confidence intervals includes zero, no significant difference in detection accuracy between uncompressed and object-based SPIHT 80:1 compression was observed at a P value of 5%. The F test of the null hypothesis that all of the modes (uncompressed and four compressed modes) were equivalent yielded the following results: F = 0.255, P = .903 for the microcalcification finding; F = 0.340, P = .848 for the mass finding; and F = 0.122, P = .975 for both findings.

CONCLUSION

To within the accuracy of these measurements, lossy compression of digital mammographic data at 80:1 with JPEG2000 or the object-based SPIHT algorithm can be performed without decreasing the rate of detection of clusters of microcalcifications and masses.

Performance and reproducibility of a computerized mass detection scheme for digitized mammography using rotated and resampled images: an assessment

OBJECTIVE

Our objective was to compare the performance and reproducibility of a computer-aided detection (CAD) scheme that uses multiple rotated and resampled images with an in-house-developed CAD scheme (single-image-based) and a commercial CAD product in detecting masses depicted on digitized mammograms.

MATERIALS AND METHODS

Ninety-two film mammograms (acquired from 23 patients) were selected. Forty-four mass regions associated with malignancy were visually identified. A commercial CAD system was used to scan and process each image four times, for a total of 368 digitized images depicting 176 mass regions. Images were processed using two CAD schemes developed in our laboratory. One uses the detection results generated from a single image, and the other averages five detection scores generated after processing the originally digitized image and four slightly rotated and resampled images. A region-based analysis was used to compare reproducibility and performance levels among the two in-house schemes and the commercial system.

RESULTS

The commercial system detected a total of 98 mass regions (55.7% sensitivity) and 136 false-positive regions (an average of 0.37 per image). Among the detected mass regions, 76 represented 19 regions that were detected on all four scans and 22 represented 10 regions that were not fully reproducible. Eighty-eight false-positive detections represented 22 reproducible detections on all four scans. Our single-image-based scheme identified 87 mass regions and 160 false-positive regions. Seventeen mass regions and 28 false-positive regions were detected on all four scans. The multiple-image-based scheme identified 98 mass regions and 132 false-positive regions. Twenty-three mass regions were detected on all four scans. One hundred twelve of the 132 false-positive regions represented 28 reproducible detections.

CONCLUSION

Averaging detection scores from multiple rotated and resampled images generated from a single digitization of a film can reduce variations in detection scores. Our multiple-image-based scheme improved both performance and reproducibility over the single-image-based scheme. The multiple-image-based scheme yielded an overall performance comparable to that of the commercial system but with improved reproducibility.

A method to test the reproducibility and to improve performance of computer-aided detection schemes for digitized mammograms

The purpose of this study is to develop a new method for assessment of the reproducibility of computer-aided detection (CAD) schemes for digitized mammograms and to evaluate the possibility of using the implemented approach for improving CAD performance. Two thousand digitized mammograms (representing 500 cases) with 300 depicted verified masses were selected in the study. Series of images were generated for each digitized image by resampling after a series of slight image rotations. A CAD scheme developed in our laboratory was applied to all images to detect suspicious mass regions. We evaluated the reproducibility of the scheme using the detection sensitivity and false-positive rates for the original and resampled images. We also explored the possibility of improving CAD performance using three methods of combining results from the original and resampled images, including simple grouping, averaging output scores, and averaging output scores after grouping. The CAD scheme generated a detection score (from 0 to 1) for each identified suspicious region. A region with a detection score >0.5 was considered as positive. The CAD scheme detected 238 masses (79.3% case-based sensitivity) and identified 1093 false-positive regions (average 0.55 per image) in the original image dataset. In eleven repeated tests using original and ten sets of rotated and resampled images, the scheme detected a maximum of 271 masses and identified as many as 2359 false-positive regions. Two hundred and eighteen masses (80.4%) and 618 false-positive regions (26.2%) were detected in all 11 sets of images. Combining detection results improved reproducibility and the overall CAD performance. In the range of an average false-positive detection rate between 0.5 and 1 per image, the sensitivity of the scheme could be increased approximately 5% after averaging the scores of the regions detected in at least four images. At low false-positive rate (e.g., < or =average 0.3 per image), the grouping method alone could increase CAD sensitivity by 7%. The study demonstrated that reproducibility of a CAD scheme can be tested using a set of slightly rotated and resampled images. Because the reproducibility of true-positive detections is generally higher than that of false-positive detections, combining detection results generated from subsets of rotated and resampled images could improve both reproducibility and overall performance of CAD schemes.

Image compression in morphometry studies requiring 21 CFR Part 11 compliance: procedure is key with TIFFs and various JPEG compression strengths

This study aims to compare the integrity and reproducibility of measurements created from uncompressed and compressed digital images in order to implement compliance with 21 CFR Part 11 for image analysis studies executed using 21 CFR Part 58 compliant capture systems. Images of a 400-mesh electron microscope grid and H&E stained rat liver tissue were captured on an upright microscope with digital camera using commercially available analysis software. Digital images were stored as either uncompressed TIFFs or in one of five different levels of JPEG compression. The grid images were analyzed with automatic detection of bright objects while the liver images were segmented using color cube-based morphometry techniques, respectively, using commercially-available image analysis software. When comparing the feature-extracted measurements from the TIFF uncompressed to the JPEG compressed images, the data suggest that JPEG compression does not alter the accuracy or reliability to reproduce individual data point measurements in all but the highest compression levels. There is, however, discordance if the initial measure was obtained with a TIFF format and subsequently saved as one of the JPEG levels, suggesting that the use of compression must precede feature extraction. It is a common practice in software packages to work with TIFF uncompressed images. However, this study suggests that the use of JPEG compression as part of the analysis work flow was an acceptable practice for these images and features. Investigators applying image file compression to other organ images will need to validate the utility of image compression in their work flow. A procedure to digitally acquire and JPEG compress images prior to image analysis has the potential to reduce file archiving demands without compromising reproducibility of data.

Computer-aided detection performance in mammographic examination of masses: assessment

PURPOSE

To compare performance of two computer-aided detection (CAD) systems and an in-house scheme applied to five groups of sequentially acquired screening mammograms.

MATERIALS AND METHODS

Two hundred nineteen film-based mammographic examinations, classified into five groups, were included in this study. Group 1 included 58 examinations in which verified malignant masses were detected during screening; group 2, 39 in which all available latest examinations were performed prior to diagnosis of these malignant masses (subset of 39 women from group 1); group 3, 22 in which findings were interpreted as negative but were verified as cancer within 1 year from the negative interpretation (missed cancers); group 4, 50 in which findings were negative and patients were not recalled for additional procedures; and group 5, 50 in which patients were recalled for additional procedures and findings were negative for cancer. In all examinations, images were processed with two Food and Drug Administration-approved commercially available CAD systems and an in-house scheme. Performance levels in terms of true-positive detection rates and number of false-positive identifications per image and per examination were compared.

RESULTS

Mass detection rates in positive examinations (group 1) were 67%-72%. Detection rates among three systems were not significantly different (P > .05). In 50 negative screening examinations (group 4), false-positive rates ranged from 1.08 to 1.68 per four-view examination. Performance level differences among systems were significant for false-positive rates (P = .008). Performance of all systems was at levels lower than publicly suggested in some retrospective studies. False-positive CAD cueing rates were significantly higher for negative examinations in which patients were recalled (group 5) than they were for those in which patients were not recalled (group 4) (P < or = .002).

CONCLUSION

Performance of CAD systems for mass detection at mammography varies significantly, depending on examination and system used. Actual performance of all systems in clinical environment can be improved.

Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments1

RATIONALE AND OBJECTIVES

The authors evaluated the impact of different computer-aided detection (CAD) cueing conditions on radiologists' performance levels in detecting and classifying masses depicted on mammograms.

MATERIALS AND METHODS

In an observer performance study, eight radiologists interpreted 110 subtle cases six times under different display conditions to detect depicted masses and classify them as benign or malignant. Forty-five cases depicted biopsy-proven masses and 65 were negative. One mass-based cueing sensitivity of 80% and two false-positive cueing rates of 1.2 and 0.5 per image were used in this study. In one mode, radiologists first interpreted images without CAD results, followed by the display of cues and reinterpretation. In another mode, radiologists viewed CAD cues as images were presented and then interpreted images. Free-response receiver operating characteristic method was used to analyze and compare detection performance. The receiver operating characteristic method was used to evaluate classification performance.

RESULTS

At these performance levels, providing cues after initial interpretation had little effect on the overall performance in detecting masses. However, in the mode with the highest false-positive cueing rate, viewing CAD cues immediately upon display of images significantly reduced average performance for both detection and classification tasks (P < .05). Viewing CAD cues during the initial display consistently resulted in fewer abnormalities being identified in noncued regions.

CONCLUSION

CAD systems with low sensitivity (< or = 80% on mass-based detection) and high false-positive rate (> or = 0.5 per image) in a dataset with subtle abnormalities had little effect on radiologists' performance in the detection and classification of mammographic masses.

Computer-Aided Detection Schemes: The Effect of Limiting the Number of Cued Regions in Each Case

OBJECTIVE

We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case.

MATERIALS AND METHODS

A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one.

RESULTS

The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%.

CONCLUSION

Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography.

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.

Performance change of mammographic CAD schemes optimized with most-recent and prior image databases

RATIONALE AND OBJECTIVES

The authors evaluated performance changes in the detection of masses on "current" (latest) and "prior" images by computer-aided diagnosis (CAD) schemes that had been optimized with databases of current and prior mammograms.

MATERIALS AND METHODS

The authors selected 260 pairs of matched consecutive mammograms. Each current image depicted one or two verified masses. All prior images had been interpreted originally as negative or probably benign. A CAD scheme initially detected 261 mass regions and 465 false-positive regions on the current images, and 252 corresponding mass regions (early signs) and 471 false-positive regions on prior images. These regions were divided into two training and two testing databases. The current and prior training databases were used to optimize two CAD schemes with a genetic algorithm. These schemes were evaluated with two independent testing databases.

RESULTS

The scheme optimized with current images produced areas under the receiver operating characteristic curve of (0.89 +/- 0.01 and 0.65 +/- 0.02 when tested with current images and prior images, respectively. The scheme optimized with prior images produced areas under the receiver operating characteristic curve of 0.81 +/- 0.02 and 0.71 +/- 0.02 when tested with current images and prior images, respectively. Performance changes for both current and prior testing databases were significant (P < .01) for the two schemes.

CONCLUSION

CAD schemes trained with current images do not perform optimally in detecting masses depicted on prior images. To optimize CAD schemes for early detection, it may be important to include in the training database a large fraction of prior images originally reported as negative and later proven to be positive.

Computer-aided detection in mammography: an assessment of performance on current and prior images

RATIONALE AND OBJECTIVES

The authors assessed and compared the performance of a computer-aided detection (CAD) scheme for the detection of masses and microcalcification clusters on a set of images collected from two consecutive ("current" and "prior") mammographic examinations.

MATERIALS AND METHODS

A previously developed CAD scheme was used to assess two consecutive screening mammograms from 200 cases in which the current mammogram showed a mass or cluster of microcalcifications that resulted in breast biopsy. The latest prior examinations had been initially interpreted as negative or definitely benign findings (Breast Imaging Reporting and Data System rating, 1 or 2). The study involved images of 400 examinations acquired in 200 patients. Radiologists identified 172 masses and 128 clusters of microcalcifications on the current images. The performance of the CAD scheme was analyzed and compared for the current and latest prior images.

RESULTS

There were significant differences (P < .01) between current and prior images in many feature values. The performance of the CAD scheme was significantly lower for prior than for current images (P < .01). At 0.5 and 0.2 false-positive mass and cluster cues per image, the scheme detected 78 malignant masses (78%) and 63 malignant clusters (80%) on current images. Only 42% of malignant cases were detected on prior images, including 40 masses (40%) and 36 microcalcification clusters (46%).

CONCLUSION

CAD schemes can detect a substantial fraction of masses and microcalcification clusters depicted on prior images. To improve performance with prior images, the scheme may have to be adaptively reoptimized with increasingly more subtle abnormalities.

Soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary findings

PURPOSE

To assess the performance of radiologists in the detection of masses and microcalcification clusters on digitized mammograms by using different computer-assisted detection (CAD) cuing environments.

MATERIALS AND METHODS

Two hundred nine digitized mammograms depicting 57 verified masses and 38 microcalcification clusters in 85 positive and 35 negative cases were interpreted independently by seven radiologists using five display modes. Except for the first mode, for which no CAD results were provided, suspicious regions identified with a CAD scheme were cued in all the other modes by using a combination of two cuing sensitivities (90% and 50%) and two false-positive rates (0.5 and 2.0 per image). A receiver operating characteristic study was performed by using soft-copy images.

RESULTS

CAD cuing at 90% sensitivity and a rate of 0.5 false-positive region per image improved observer performance levels significantly (P < .01). As accuracy of CAD cuing decreased so did observer performances (P < .01). Cuing specificity affected mass detection more significantly, while cuing sensitivity affected detection of microcalcification clusters more significantly (P < .01). Reduction of cuing sensitivity and specificity significantly increased false-negative rates in noncued areas (P < .05). Trends were consistent for all observers.

CONCLUSION

CAD systems have the potential to significantly improve diagnostic performance in mammography. However, poorly performing schemes could adversely affect observer performance in both cued and noncued areas.

Performance gain in computer-assisted detection schemes by averaging scores generated from artificial neural networks with adaptive filtering

The authors investigated a new method to optimize artificial neural networks (ANNs) with adaptive filtering used in computer-assisted detection schemes in digitized mammograms and to assess performance changes when averaging classification scores from three sets of optimized schemes. Two independent training and testing image databases involving 978 and 830 digitized mammograms, respectively, were used in this study. In the training data set, initial filtering and subtraction resulted in the identification of 592 mass regions and 3790 suspicious, but actually negative regions. These regions (including both true-positive and negative regions) were segmented into three subsets three times based on the calculation of the values of three features as segmentation indices. The indices were "mass" size multiplied by their digital value contrast, conspicuity, and circularity. Nine ANN-based classifiers were separately optimized using a genetic algorithm for each subset of regions. Each region was assigned three classification scores after applying the three adaptive ANNs. The performance gain of the CAD scheme after averaging the three scores for each suspicious region was tested using an independent data set and a ROC methodology. The experimental results showed that the areas under ROC curves (Az) for the testing database using three sets of optimized ANNs individually were 0.84+/-0.01, 0.83+/-0.01, and 0.84+/-0.01, respectively. The between-index correlations of three A values were 0.013, -0.007, and 0.086. Similar to averaging diagnostic ratings from independent observers, by averaging three ANN-generated scores for each testing region, the performance of the CAD scheme was significantly improved (p<0.001) with Az value of 0.95+/-0.01.