Matthias Elter

CADx of mammographic masses and clustered microcalcifications: a review.

Breast cancer is the most common type of cancer among women in the western world. While mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, the interpretation of mammograms is a difficult and error-prone task. Hence, computer aids have been developed that assist the radiologist in the interpretation of mammograms. Computer-aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer-aided diagnosis (CADx) systems have been proposed that assist the radiologist in the classification of mammographic lesions as benign or malignant. While a broad variety of approaches to both CADe and CADx systems have been published in the past two decades, an extensive survey of the state of the art is only available for CADe approaches. Therefore, a comprehensive review of the state of the art of CADx approaches is presented in this work. Besides providing a summary, the goals for this article are to identify relations, contradictions, and gaps in literature, and to suggest directions for future research. Because of the vast amount of publications on the topic, this survey is restricted to the two most important types of mammographic lesions: masses and clustered microcalcifications. Furthermore, it focuses on articles published in international journals.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.

Characterizing mammographic images by using generic texture features

IntroductionAlthough mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design.MethodsA case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model.ResultsOf the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model.ConclusionsUsing texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.

Automatic Adaptive Enhancement for Images Obtained With Fiberscopic Endoscopes

Modern techniques for medical diagnostics and therapy in keyhole surgery scenarios as well as technical inspection make use of flexible endoscopes. Their characteristic bendable image conductor consists of a very limited number of coated fibers, which leads to so-called comb structure. This effect has a negative impact on further image processing steps such as feature tracking because these overlaid image structures are wrongly detected as image features. With respect to these tasks, we propose an automatic approach to generate optimal spectral filter masks for enhancement of fiberscopic images. We apply the Nyquist-Shannon sampling theorem to the spectrum of fiberscopically acquired images to obtain parameters for optimal filter mask calculation. This can be done automatically and independently of scale and resolution of the image conductor as well as type and resolution of the image sensor. We designed and verified simple rotation invariant masks as well as star-shaped rotation variant masks that contain information about orientation between the fiberscope and sensor. A subjective survey among experts between different modes of filtering certified the best results to the adapted star-shaped mask for high-quality glass fiberscopes. We furthermore define an objective metric to evaluate the results of different filter approaches, which verifies the results of the subjective survey. The proposed approach enables the automated reduction of fiberscopic comb structure. It is adaptive to arbitrary endoscope and sensor combinations. The results give the prospect of a large field of possible applications to reduce fiberscopic structure both for visual optimization in clinical environments and for further digital imaging tasks

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies.

IntroductionBreast cancer is globally a major threat for women’s health. Screening and adequate follow-up can significantly reduce the mortality from breast cancer. Human second reading of screening mammograms can increase breast cancer detection rates, whereas this has not been proven for current computer-aided detection systems as “second reader”. Critical factors include the detection accuracy of the systems and the screening experience and training of the radiologist with the system. When assessing the performance of systems and system components, the choice of evaluation methods is particularly critical. Core assets herein are reference image databases and statistical methods.MethodsWe have analyzed characteristics and usage of the currently largest publicly available mammography database, the Digital Database for Screening Mammography (DDSM) from the University of South Florida, in literature indexed in Medline, IEEE Xplore, SpringerLink, and SPIE, with respect to type of computer-aided diagnosis (CAD) (detection, CADe, or diagnostics, CADx), selection of database subsets, choice of evaluation method, and quality of descriptions.Results59 publications presenting 106 evaluation studies met our selection criteria. In 54 studies (50.9%), the selection of test items (cases, images, regions of interest) extracted from the DDSM was not reproducible. Only 2 CADx studies, not any CADe studies, used the entire DDSM. The number of test items varies from 100 to 6000. Different statistical evaluation methods are chosen. Most common are train/test (34.9% of the studies), leave-one-out (23.6%), and N-fold cross-validation (18.9%). Database-related terminology tends to be imprecise or ambiguous, especially regarding the term “case”.DiscussionOverall, both the use of the DDSM as data source for evaluation of mammography CAD systems, and the application of statistical evaluation methods were found highly diverse. Results reported from different studies are therefore hardly comparable. Drawbacks of the DDSM (e.g. varying quality of lesion annotations) may contribute to the reasons. But larger bias seems to be caused by authors’ own decisions upon study design.Recommendations/conclusionFor future evaluation studies, we derive a set of 13 recommendations concerning the construction and usage of a test database, as well as the application of statistical evaluation methods.

Detection of malaria parasites in thick blood films

Malaria, caused by a blood parasite of the genus plasmodium, kills millions of people each year. According to the World Health Organization, the standard for malaria diagnosis is microscopic examination of a stained blood film. We have developed a two-stage algorithm for the automatic detection of plasmodia in thick blood films. The focus of the first stage is on high detection sensitivity while accepting high numbers of false-positive detections per image. The second stage reduces the number of false-positive detections to an acceptable level while maintaining the detection sensitivity of the first stage. The algorithm can detect plasmodia at a sensitivity of 0.97 with a mean number of 0.8 false-positive detections per image. Our results indicate that the proposed algorithm is suitable for the development of an automated microscope for computer-aided malaria screening.

Physically Motivated Reconstruction of Fiberscopic Images

Flexible endoscopes are applied in modern techniques for technical inspection as well as medical diagnostic and therapy in keyhole-surgery-scenarios. Their characteristic bendable image conductor consists of a limited number of coated fibers. An optical fiber consists of a core surrounded by a cladding layer. This configuration leads to imaging artifacts, called comb structures. They have a negative impact on further image processing steps, like feature detection and tracking. The intensity distribution of a cross-section of a fiber is usually modeled by a two-dimensional Gaussian. We propose a preprocessing algorithm which exploits this physical property to remove the comb structure while retaining the image content. The proposed approach effectively removes fiberscopic comb structure in real-time. It is adaptive to arbitrary endoscope and sensor combinations. The results give prospect of a large field of possible applications both for visual optimization in the clinical environment and for further digital imaging tasks

Semiautomatic segmentation for the computer aided diagnosis of clustered microcalcifications

Screening mammography is recognized as the most effective tool for early breast cancer detection. However, its application in clinical practice shows some of its weaknesses. While clustered microcalcifications are often an early sign of breast cancer, the discrimination of benign from malignant clusters based on their appearance in mammograms is a very difficult task. Hence, it is not surprising that typically only 15% to 30% of breast biopsies performed on calcifications will be positive for malignancy. As this low positive predictive value of mammography regarding the diagnosis of calcification clusters results in many unnecessary biopsies performed on benign calcifications, we propose a novel computer aided diagnosis (CADx) approach with the goal to improve the reliability of microcalcification classification. As effective automatic classification of microcalcification clusters relies on good segmentations of the individual calcification particles, many approaches to the automatic segmentation of individual particles have been proposed in the past. Because none of the fully automatic approaches seem to result in optimal segmentations, we propose a novel semiautomatic approach that has automatic components but also allows some interaction of the radiologist. Based on the resulting segmentations we extract a broad range of features that characterize the morphology and distribution of calcification particles. Using regions of interest containing either benign or malignant clusters extracted from the digital database for screening mammography we evaluate the performance of our approach using a support vector machine and ROC analysis. The resulting ROC performance is very promising and we show that the performance of our semiautomatic segmentation is significantly higher than that of a comparable fully automatic approach.

Evaluation of spatial interpolation strategies for the removal of comb-structure in fiber-optic images

Modern techniques for medical diagnosis and therapy in minimal invasive surgery scenarios as well as industrial inspection make considerable use of flexible, fiberoptic endoscopes in order to gain visual access to holes, hollows, antrums and cavities that are difficult to enter and examine. Unfortunately, fiber-optic endoscopes exhibit artifacts in the images that hinder or at worst prevent fundamental image analysis techniques. The dark comb-like artifacts originate from the opaque cladding layer surrounding each single fiber in the image conductor. Although the removal of comb structure is crucial for fiber-optic image analysis, literature covers only a few approaches. Those are based on Fourier analysis and make use of spectral masking or they operate in the spatial domain and rely on interpolation. In this paper, we concentrate on the latter type and introduce interpolation concepts known from related disciplines to the task of comb structure removal. For a quantitative evaluation, we perform experiments with real images as well as with bivariate test functions and rate an algorithm’s performance in terms of the normalized root mean square error - a quality metric that it is most commonly used in signal processing for this purpose. Hence, this paper counters the fact that literature lacks an objective performance comparison of the state-of-the-art interpolation based approaches for this type of application.

A knowledge-based approach to the CADx of mammographic masses

Today, mammography is recognized as the most effective technique for breast cancer screening. Unfortunately, the low positive predictive value of breast biopsy examinations resulting from mammogram interpretation leads to many unnecessary biopsies performed on benign lesions. In the last years, several computer assisted diagnosis (CADx) systems have been proposed with the goal to assist the radiologist in the discrimination of benign and malignant breast lesions and thus to reduce the high number of unnecessary biopsies. In this paper we present a novel, knowledge-based approach to the computer aided discrimination of mammographic mass lesions that uses computer-extracted attributes of mammographic masses and clinical data as input attributes to a case-based reasoning system. Our approach emphasizes a transparent reasoning process which is important for the acceptance of a CADx system in clinical practice. We evaluate the performance of the proposed system on a large publicly available mammography database using receiver operating characteristic curve analysis. Our results indicate that the proposed CADx system has the potential to significantly reduce the number of unnecessary breast biopsies in clinical practice.