Computerized detection of masses from digitized mammograms: comparison of single-image segmentation and bilateral-image subtraction

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning

PURPOSE

In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.

MATERIALS AND METHODS

We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. The test dataset was created by shifting some of the training data images. Thus, although both datasets were different, they retained a deep structural similarity. We then applied our trained deep-learning model to detect masses on 8-bit mammogram images containing malignant masses. The input images were preprocessed by applying a scaling parameter of intensity before being used to train the CNN model for mass differentiation.

RESULTS

The highest area under the receiver operating characteristic curve was 0.897 (Î 20).

CONCLUSION

Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.

Optical mammography: bilateral breast symmetry in hemoglobin saturation maps

We present a study of the bilateral symmetry of human breast hemoglobin saturation maps measured with a broadband optical mammography instrument. We have imaged 21 patients with unilateral breast cancer, 32 patients with unilateral benign lesions, and 27 healthy patients. An image registration process was applied to the bilateral hemoglobin saturation (SO 2 SO2 ) images by assigning each pixel to the low, middle, or high range of SO 2 SO2 values, where the thresholds for the categories were the 15th and 85th percentiles of the individual saturation range. The Dice coefficient, which is a measure of similarity, was calculated for each patient’s pair of right and left breast SO 2 SO2 images. The invasive cancer patients were found to have an average Dice coefficient value of 0.55±0.07 0.55±0.07 , which was significantly lower than the benign and healthy groups (0.61±0.11 0.61±0.11 and 0.62±0.12 0.62±0.12 , respectively). Although differences were seen in a group analysis, the healthy patient Dice coefficients spanned a wide range, limiting the diagnostic capabilities of this SO 2 SO2 symmetry analysis on an individual basis. Our results suggest that for assessing the SO 2 SO2 contrast of breast lesions, it may be better to select a reference tissue in the ipsilateral rather than the contralateral breast.

Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment

RATIONALE AND OBJECTIVES

Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information.

MATERIALS AND METHODS

A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 were positive cases with verified cancer associated with malignant masses and 300 were negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic type data analysis method.

RESULTS

CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based).

CONCLUSIONS

This study indicated that 1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and 2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.

Computer-aided detection scheme for sentinel lymph nodes in lymphoscintigrams using symmetrical property around mapped injection point

It is difficult to detect sentinel lymph nodes (SLNs) around an injection point of radiopharmaceuticals mapped in lymphoscintigrams. The purpose of this study was to develop a computer-aided detection (CAD) scheme for SLNs by a subtraction technique using the symmetrical property in the mapped injection point. Our database consisted of 78 lymphoscintigrams with 86 SLNs. In our CAD scheme, the mapped injection point of radiopharmaceuticals was first segmented from the lymphoscintigram using a gray-level thresholding technique. Lymphoscintigram was then divided into four regions by vertical and horizontal straight lines through the center of the segmented injection point. One of the four divided regions was defined as the target region. The correlation coefficients based on pixel values were calculated between the target region and each of the other three regions. The region with the highest correlation coefficient among three regions was selected as the similar region to the target region. The values of pixels on the target region were subtracted by the values of the corresponding pixels on the similar region. This procedure was repeated until every divided region had been used as target region. SLNs were segmented by applying a gray-level thresholding technique to the subtracted image. With our CAD scheme, sensitivity and the number of false positives were 95.3% (82/86) and 2.51 per image, respectively. Our CAD scheme achieved a high level of detection accuracy, and would have a great potential in assisting physicians to detect SLNs in lymphoscintigrams.

Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry

This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of cranio-caudal (CC) and medio-lateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781±0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6-18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry.

Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification

RATIONALE AND OBJECTIVES

Assessment of the breast tissue pattern asymmetry depicted on bilateral mammograms is routinely used by radiologists when reading and interpreting mammograms. The purpose of this study is to develop an automated scheme to detect breast tissue asymmetry depicted on bilateral mammograms and use the computed asymmetric features to predict the likelihood (or the risk) of women having or developing breast abnormalities or cancer.

MATERIALS AND METHODS

A testing dataset was selected from a large and diverse full-field digital mammography image database, which includes 100 randomly selected negative cases (not recalled during the screening) and 100 positive cases for having or developing breast abnormalities or cancer. Among these positive cases 40 were recalled (biopsy) because of suspicious findings in which 8 were determined as high risk with the lesions surgically removed and the remaining were proven to be benign, and 60 cases were acquired from examinations that were interpreted as negative (without dominant masses or microcalcifications) but the cancers were detected 6-18 months later. A computerized scheme was developed to detect asymmetry of mammographic tissue density represented by the related feature differences computed from bilateral images. Initially, each of 20 features was tested to classify between the positive and the negative cases. To further improve the classification performance, a genetic algorithm (GA) was applied to select a set of optimal features and build an artificial neural network (ANN). The leave-one-case-out validation method was used to evaluate the ANN classification performance.

RESULTS

Using a single feature, the maximum classification performance level measured by the area under the receiver operating characteristic curve (AUC) was 0.681 ± 0.038. Using the GA-optimized ANN, the classification performance level increased to an AUC = 0.754 ± 0.024. At 90% specificity, the ANN classifier yielded 42% sensitivity, in which 42 positive cases were correctly identified. Among them, 30 were the "prior" examinations of the cancer cases and 12 were recalled benign cases, which represent 50% and 30% sensitivity levels in these two subgroups, respectively.

CONCLUSIONS

This study demonstrated that using the computerized detected feature differences related to the bilateral mammographic breast tissue asymmetry, an automated scheme is able to classify a set of testing cases into the two groups of positive or negative of having or developing breast abnormalities or cancer. Hence, further development and optimization of this automated method may eventually help radiologists identify a fraction of women at high risk of developing breast cancer and ultimately detect cancer at an early stage.

Image analysis in medical imaging: recent advances in selected examples

Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments.

Knowledge-based computer-aided detection of masses on digitized mammograms: a preliminary assessment

The purpose of this work was to develop and evaluate a computer-aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge-based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule-based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring "similarity" between a suspicious region and a "known" mass, then deriving a composite "likelihood" measure based on all "known" masses to determine the state of the suspicious region. To assess the performance of this method, receiver-operating characteristic (ROC) analyses were employed. Using a leave-one-out validation method with the development set of 600 regions, the knowledge-based CAD scheme achieved an area under the ROC curve of 0.83. Fifty-one percent of the previously identified false-positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1,000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge-based approaches can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule-based CAD schemes.

Computer-aided detection and diagnosis of breast cancer

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Analysis of clustered microcalcifications by using a single numeric classifier extracted from mammographic digital images

RATIONALE AND OBJECTIVES

The authors prospectively tested the performance of a single numeric classifier constructed from a discriminative analysis classification system based on automatic computer-extracted quantitative features of clustered microcalcifications.

MATERIALS AND METHODS

Mammographically detected clustered microcalcifications in patients who had been referred for biopsy were digitized at 600 dpi with an 8-bit gray scale. A software program was developed to extract features automatically from digitized images to describe the clustered microcalcifications quantitatively. The significance of these features was evaluated by using the Wilcoxon test, the Welch modified two-sample t test, and the two-sample Kolmogorov-Smirnov test. A discriminant analysis pattern recognition system was constructed to generate a single numeric classifier for each case, based on the extracted features. This system was trained on 137 archival known reference cases and its performance tested on 24 unknown prospective cases. The results were evaluated by using receiver operating characteristic analysis.

RESULTS

Thirty-seven extracted parameters demonstrated a statistically significant difference between the values for the benign and for the malignant lesions. Seven independent factors were selected to construct the classifier and to evaluate the unknown prospective cases. The area under the receiver operating characteristic curve for the prospective cases was 0.88.

CONCLUSION

A pattern recognition classifier based on quantitative features for clustered microcalcifications at screen-film mammography was found to perform satisfactorily. The software may be of value in the interpretation of mammographically detected microcalcifications.