A regional registration method to find corresponding mass lesions in temporal mammogram pairs

A new idea for visualization of lesions distribution in mammogram based on CPD registration method

BACKGROUND

Mammography is currently the most effective technique for breast cancer. Lesions distribution can provide support for clinical diagnosis and epidemiological studies.

OBJECTIVE

We presented a new idea to help radiologists study breast lesions distribution conveniently. We also developed an automatic tool based on this idea which could show visualization of lesions distribution in a standard mammogram.

METHODS

Firstly, establishing a lesion database to study; then, extracting breast contours and match different women's mammograms to a standard mammogram; finally, showing the lesion distribution in the standard mammogram, and providing the distribution statistics. The crucial process of developing this tool was matching different women's mammograms correctly. We used a hybrid breast contour extraction method combined with coherent point drift method to match different women's mammograms.

RESULTS

We tested our automatic tool by four mass datasets of 641 images. The distribution results shown by the tool were consistent with the results counted according to their reports and mammograms by manual. We also discussed the registration error that was less than 3.3 mm in average distance.

CONCLUSIONS

The new idea is effective and the automatic tool can provide lesions distribution results which are consistent with radiologists simply and conveniently.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Optimizing Case-based detection performance in a multiview CAD system for mammography

When reading mammograms, radiologists combine information from multiple views to detect abnormalities. Most computer-aided detection (CAD) systems, however, use primitive methods for inclusion of multiview context or analyze each view independently. In previous research it was found that in mammography lesion-based detection performance of CAD systems can be improved when correspondences between MLO and CC views are taken into account. However, detection at case level detection did not improve. In this paper, we propose a new learning method for multiview CAD systems, which is aimed at optimizing case-based detection performance. The method builds on a single-view lesion detection system and a correspondence classifier. The latter provides class probabilities for the various types of region pairs and correspondence features. The correspondence classifier output is used to bias the selection of training patterns for a multiview CAD system. In this way training can be forced to focus on optimization of case-based detection performance. The method is applied to the problem of detecting malignant masses and architectural distortions. Experiments involve 454 mammograms consisting of four views with a malignant region visible in at least one of the views. To evaluate performance, five-fold cross validation and FROC analysis was performed. Bootstrapping was used for statistical analysis. A significant increase of case-based detection performance was found when the proposed method was used. Mean sensitivity increased by 4.7% in the range of 0.01-0.5 false positives per image.

Computer-aided diagnosis with temporal analysis to improve radiologists' interpretation of mammographic mass lesions

The purpose of this study was to evaluate the effect of independent reading with computer-aided diagnosis (CAD) and independent double reading on radiologists' performance to characterize mass lesions on serial mammograms. Six radiologists rated 198 cases, 99 benign and 99 malignant. For each case, the mammograms from two consecutive screening rounds were available. The mass was visible on the prior view in 40% of the cases. Independently, a CAD programe also rated each mass lesion making use of information from prior and current views. The following reading situations were compared: single reading, independent reading with CAD, and independent double reading. Independent reading with CAD was implemented by averaging the scaled ratings from each radiologist and the scaled CAD scores. We implemented independent double reading by averaging the scaled scores from two radiologists. Results were evaluated using receiver-operating characteristic (ROC) methodology and multiple reader multiple case analysis. The average performance, measured as the area under the ROC curve (A(z) value), was 0.80 for the single-reading mode. For independent double reading, the average performance improved to 0.81. This improvement was not significant. For independent interpretation with CAD, the average performance significantly increased to 0.83 (P < 0.05). We conclude that CAD technology with temporal analysis has the potential to help radiologists with the task of discriminating between benign and malignant masses.

Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases

Automated analysis of mammograms requires robust methods for pectoralis segmentation and nipple detection. Locating the nipple is especially important in multiview computer aided detection systems, in which findings are matched across images using the nipple-to-finding distance. Segmenting the pectoralis is a key preprocessing step to avoid false positives when detecting masses due to the similarity of the texture of mammographic parenchyma and the pectoral muscle. A multiatlas algorithm capable of providing very robust initial estimates of the nipple position and pectoral region in digitized mammograms is presented here. Ten full-field digital mammograms, which are easily annotated attributed to their excellent contrast, are robustly registered to the target digitized film-screen mammogram. The annotations are then propagated and fused into a final nipple position and pectoralis segmentation. Compared to other nipple detection methods in the literature, the system proposed here has the advantages that it is more robust and can provide a reliable estimate when the nipple is located outside the image. Our results show that the change in the correlation between nipple-to-finding distances in craniocaudal and mediolateral oblique views is not significant when the detected nipple positions replace the manual annotations. Moreover, the pectoralis segmentation is acceptable and can be used as initialization for a more complex algorithm to optimize the outline locally. A novel aspect of the method is that it is also capable of detecting and segmenting the pectoralis in craniocaudal views.

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

Advances in computer-aided diagnosis for breast cancer

PURPOSE OF REVIEW

Computer-aided diagnosis (CAD) is a technology used for the detection and characterization of cancer. Although CAD is not limited to a single type of cancer, a large number of CAD systems to date have been designed and used for breast cancer. The aim of this review is to discuss the current state of the CAD systems for breast-cancer diagnosis, their application as a second reader in clinical practice, and studies that have evaluated the effect of CAD on radiologists' performance.

RECENT FINDINGS

A large number of CAD applications are being developed for different imaging modalities. Owing to commercially available Food and Drug Administration (FDA) approved systems, the main clinical use of CAD to date is for screen-film mammography. Many studies have shown that CAD improves radiologists' performance. A large number of academic institutions have devoted a substantial research effort to developing CAD methods.

SUMMARY

CAD systems will play an increasingly important role in the clinic as a second reader. Clinical trials have shown that CAD can improve the accuracy of breast-cancer detection. Preclinical studies have demonstrated the potential of CAD to improve the classification of malignant and benign lesions. An increased number of CAD systems are being developed for different breast-imaging modalities.

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Temporal change analysis for characterization of mass lesions in mammography

In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average A(z) value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.

Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views

In this paper we present a method to link potentially suspicious mass regions detected by a Computer-Aided Detection (CAD) scheme in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For all possible combinations of mass candidate regions, a number of features are determined. These features include the difference in the radial distance from the candidate regions to the nipple, the gray scale correlation between both regions, and the mass likelihood of the regions determined by the single view CAD scheme. Linear Discriminant Analysis (LDA) is used to discriminate between correct and incorrect links. The method was tested on a set of 412 cancer cases. In each case a malignant mass, architectural distortion, or asymmetry was annotated. In 92% of these cases the candidate mass detections by CAD included the cancer regions in both views. It was found that in 82% of the cases a correct link between the true positive regions in both views could be established by our method. Possible applications of the method may be found in multiple view analysis to improve CAD results, and for the presentation of CAD results to the radiologist on a mammography workstation.