Single and multiscale detection of masses in digital mammograms

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.

An Effective Two Way Classification of Breast Cancer Images: A Detailed Review

Cancer, a disease of cells, causes cell growth which differs from normal cell growth ratio, this cell growth spreads in the human body and kills the body cells. Breast cancer, it’s a highly heterogeneous disease and western women commonly witness this. Mammography, a pre-screening X-ray based check is used to diagnose woman’s breast cancer. This basic test mode helps in identifying breast cancer at early stage and this early stage detection would support in recovering more number of women from this serious disease. Medical centres deputed highly skilled radiologists and they were given the responsibility of analysing this mammography results but still human errors are inevitable. An error frequency ratio is high when radiologists exhausted in their analysis task and leads variations in either observations ie., internal or external observation. Also, quality of the image plays vital role in Mammographic sensitivity and leads to variation. Several automation processes were tried in streamlining and standardising diagnosis analysis process and quality of breast cancer images were improved. This paper inducts a two way mode algorithm for grouping of breast cancer images to 1. benign (tumour growing, but not dangerous) and 2. malignant (cannot be controlled, it causes death) classes. Two-way mode data mining algorithms are used due to thinly dispersed distribution of abnormal mammograms. First type algorithm is k-means algorithm, which regroups the given data elements into clusters (ie., prioritized by the users). Second type algorithm is Support Vector Machine (SVM), which is used to identify the most suitable function which differentiates the members based on the training data.

iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading Mammograms

This study introduces an individualized tool for identifying mammogram interpretation errors, called eye-Computer Assisted Perception (iCAP). iCAP consists of two modules, one which processes areas marked by radiologists as suspicious for cancer and classifies these as False Positive (FP) or True Positive (TP) decisions, while the second module classifies fixated but not marked locations as False Negative (FN) or True-Negative (TN) decisions. iCAP relies on both radiologists' gaze-related parameters, extracted from eye tracking data, and image-based features. In order to evaluate iCAP, eye tracking data from eight breast radiologists reading 120 two-view digital mammograms were collected. Fifty-nine cases had biopsy proven cancer. For each radiologist, a user-specific support vector machine model was built to classify the radiologist' s reported areas as TPs or FPs and fixated locations as TNs or FNs. The performances of the classifiers were evaluated by utilizing leave-one-out cross validation. iCAP was tested retrospectively in a simulated scenario in which it was assumed that the radiologists would accept all iCAP decisions. Using iCAP led to an average increase of 12%±6% in the number of correctly localized cancer and an average decrease of 44.5%±22.7% in the number of FPs per image.

Computerized Detection of Mass Lesions in Digital Breast Tomosynthesis Images Using Two- and Three Dimensional Radial Gradient Index Segmentation

Initial results for a computerized mass lesion detection scheme for digital breast tomosynthesis (DBT) images are presented. The algorithm uses a radial gradient index feature for the initial lesion detection and for segmentation of lesion candidates. A set of features is extracted for each segmented partition. Performance of two- and three dimensional features was compared. For gradient features, the additional dimension provided no improvement in classification performance. For shape features, classification using 3D features was improved compared to the 2D equivalent features. The preliminary overall performance was 76% sensitivity at 11 false positives per exam, estimated based on DBT image data of 21 masses. A larger database will allow for further development and improvement in our computer aided detection scheme.

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.

Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms

PURPOSE

To develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) which can make use of an existing CAD system for detection of breast masses in full-field digital mammography (FFDM). This approach has the advantage that large digital screening databases that are becoming available can be used for training. DBT is currently not used for screening which makes it hard to obtain sufficient data for training.

METHODS

The proposed CAD system is applied to reconstructed DBT volumes and consists of two stages. In the first stage, an existing 2D CAD system is applied to slabs composed of multiple DBT slices, after processing the slabs to a representation similar to that of the FFDM training data. In the second stage, the authors group detections obtained in the slabs that detect the same object and determine the 3D location of the grouped findings using one of three different approaches, including one that uses a set of features extracted from the DBT slabs. Experiments were conducted to determine performance of the CAD system, the optimal slab thickness for this approach and the best method to establish the 3D location. Experiments were performed using a database of 192 patients (752 DBT volumes). In 49 patients, one or more malignancies were present which were described as a mass, architectural distortion, or asymmetry. Free response receiver operating characteristic analysis and bootstrapping were used for statistical evaluation.

RESULTS

Best performance was obtained when slab thickness was in the range of 1-2 cm. Using the feature based 3D localization procedure developed in the study, accurate 3D localization could be obtained in most cases. Case sensitivities of 80% and 90% were achieved at 0.35 and 0.99 false positives per volume, respectively.

CONCLUSIONS

This study indicates that there may be a large benefit in using 2D mammograms for the development of CAD for DBT and that there is no need to exclusively limit development to DBT data.

Detection of cancerous masses in mammograms by template matching: optimization of template brightness distribution by means of evolutionary algorithm

Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF) = 0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF = 0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI) = 2.

Marker-controlled watershed for lesion segmentation in mammograms

Lesion segmentation, which is a critical step in computer-aided diagnosis system, is a challenging task as lesion boundaries are usually obscured, irregular, and low contrast. In this paper, an accurate and robust algorithm for the automatic segmentation of breast lesions in mammograms is proposed. The traditional watershed transformation is applied to the smoothed (by the morphological reconstruction) morphological gradient image to obtain the lesion boundary in the belt between the internal and external markers. To automatically determine the internal and external markers, the rough region of the lesion is identified by a template matching and a thresholding method. Then, the internal marker is determined by performing a distance transform and the external marker by morphological dilation. The proposed algorithm is quantitatively compared to the dynamic programming boundary tracing method and the plane fitting and dynamic programming method on a set of 363 lesions (size range, 5-42 mm in diameter; mean, 15 mm), using the area overlap metric (AOM), Hausdorff distance (HD), and average minimum Euclidean distance (AMED). The mean ± SD of the values of AOM, HD, and AMED for our method were respectively 0.72 ± 0.13, 5.69 ± 2.85 mm, and 1.76 ± 1.04 mm, which is a better performance than two other proposed segmentation methods. The results also confirm the potential of the proposed algorithm to allow reliable segmentation and quantification of breast lesion in mammograms.

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then, we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust inter-modality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied "as-is" to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible.

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.

Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM

We propose a generative model for constructing an efficient set of distinctive textures for recognizing architectural distortion in digital mammograms. In the first layer of the proposed two-layer architecture, the mammogram is analyzed by a multiscale oriented filter bank to form texture descriptor of vectorized filter responses. Our model presumes that every mammogram can be characterized by a "bag of primitive texture patterns" and the set of textural primitives (or textons) is represented by a mixture of Gaussians which builds up the second layer of the proposed model. The observed textural descriptor in the first layer is assumed to be a stochastic realization of one (hard mapping) or more (soft mapping) textural primitive(s) from the second layer. The results obtained on two publicly available datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM), demonstrate the efficacy of the proposed approach.

Hybrid segmentation of mass in mammograms using template matching and dynamic programming

RATIONALE AND OBJECTIVES

Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems.

MATERIALS AND METHODS

The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided.

RESULTS

The mean overlap percentage for our proposed hybrid method was 0.727 ± 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 ± 0.216, 0.636 ± 0.190, and 0.562 ± 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001.

CONCLUSIONS

A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in interpreting mammograms.

The effect of feature selection methods on computer-aided detection of masses in mammograms

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Use of normal tissue context in computer-aided detection of masses in mammograms

When reading mammograms, radiologists do not only look at local properties of suspicious regions but also take into account more general contextual information. This suggests that context may be used to improve the performance of computer-aided detection (CAD) of malignant masses in mammograms. In this study, we developed a set of context features that represent suspiciousness of normal tissue in the same case. For each candidate mass region, three normal reference areas were defined in the image at hand. Corresponding areas were also defined in the contralateral image and in different projections. Evaluation of the context features was done using 10-fold cross validation and case based bootstrapping. Free response receiver operating characteristic (FROC) curves were computed for feature sets including context features and a feature set without context. Results show that the mean sensitivity in the interval of 0.05-0.5 false positives/image increased more than 6% when context features were added. This increase was significant ( p < 0.0001). Context computed using multiple views yielded a better performance than using a single view (mean sensitivity increase of 2.9%, p < 0.0001). Besides the importance of using multiple views, results show that best CAD performance was obtained when multiple context features were combined that are based on different reference areas in the mammogram.

Segmentation of regions of interest in mammograms in a topographic approach

This paper presents a novel method for the segmentation of regions of interest in mammograms. The algorithm concurrently delineates the boundaries of the breast boundary, the pectoral muscle, as well as dense regions that include candidate masses. The resulting representation constitutes an analysis of the global structure of the object in the mammogram. We propose a topographic representation called the isocontour map, in which a salient region forms a dense quasi-concentric pattern of contours. The topological and geometrical structure of the image is analyzed using an inclusion tree that is a hierarchical representation of the enclosure relationships between contours. The "saliency" of a region is measured topologically as the minimum nesting depth. Features at various scales are analyzed in multiscale isocontour maps, and we demonstrate that the multiscale scheme provides an efficient way of achieving better delineations. Experimental results demonstrate that the proposed method has potential as the basis for a prompting system in mammogram mass detection.

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.

Eigendetection of masses considering false positive reduction and breast density information

The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.

A technique for simulating the effect of dose reduction on image quality in digital chest radiography

PURPOSE

The purpose of this study is to provide a pragmatic tool for studying the relationship between dose and image quality in clinical chest images. To achieve this, we developed a technique for simulating the effect of dose reduction on image quality of digital chest images.

MATERIALS AND METHODS

The technique was developed for a digital charge-coupled-device (CCD) chest unit with slot-scan acquisition. Raw pixel values were scaled to a lower dose level, and a random number representing noise to each specific pixel value was added. After adding noise, raw images were post processed in the standard way. Validation was performed by comparing pixel standard deviation, as a measure of noise, in simulated images with images acquired at actual lower doses. To achieve this, a uniform test object and an anthropomorphic phantom were used. Additionally, noise power spectra of simulated and actual images were compared. Also, detectability of simulated lesions was investigated using a model observer.

RESULTS

The mean difference in noise values between simulated and real lower-dose phantom images was smaller than 5% for relevant clinical settings. Noise power spectra appeared to be comparable on average but simulated images showed slightly higher noise levels for higher spatial frequencies and slightly lower noise levels for lower spatial frequencies. Comparable detection performance was shown in simulated and actual images with slightly worse detectability for simulated lower dose images.

CONCLUSION

We have developed and validated a method for simulating dose reduction. Our method seems an acceptable pragmatic tool for studying the relationship between dose and image quality.

An evaluation and comparison of the performance of state of the art approaches for the detection of spiculated masses in mammograms

Mammography is the standard examination method for the early detection of breast cancer. In the last decade, computer assisted detection systems have been developed that assist the physician in the detection of suspicious regions in mammograms. However, recent clinical studies indicate that state of the art CAD systems might have a negative impact on the accuracy of screening mammography. Therefore, besides additional clinical studies, better evaluations of state of the art detection approaches are necessary. In this contribution three methods for the detection of spiculated masses in mammograms are evaluated and compared. All three of them are based on gradient orientation images. To detect masses, the methods use circular neighbourhoods with different sizes around a single pixel. The number of orientations in every neighbourhood is used by every method in different ways to form a result. The main contribution is the first fair comparison of the performance of different detection approaches for spiculated masses. Furthermore, a novel gradient direction analysis is introduced. The analysis is an extension to the three approaches, which increases the performance for one of the three approaches.

Combining two mammographic projections in a computer aided mass detection method

A method is presented to improve computer aided detection (CAD) results for masses in mammograms by fusing information obtained from two views of the same breast. It is based on a previously developed approach to link potentially suspicious regions in mediolateral oblique (MLO) and craniocaudal (CC) views. Using correspondence between regions, we extended our CAD scheme by building a cascaded multiple-classifier system, in which the last stage computes suspiciousness of an initially detected region conditional on the existence and similarity of a linked candidate region in the other view. We compared the two-view detection system with the single-view detection method using free-response receiver operating characteristic (FROC) analysis and cross validation. The dataset used in the evaluation consisted of 948 four-view mammograms, including 412 cancer cases with a mass, architectural distortion, or asymmetry. A statistically significant improvement was found in the lesion based detection performance. At a false positive (FP) rate of 0.1 FP/image, the lesion sensitivity improved from 56% to 61%. Case based sensitivity did not improve.

A concentric morphology model for the detection of masses in mammography

We propose a technique for the automated detection of malignant masses in screening mammography. The technique is based on the presence of concentric layers surrounding a focal area with suspicious morphological characteristics and low relative incidence in the breast region. Mammographic locations with high concentration of concentric layers with progressively lower average intensity are considered suspicious deviations from normal parenchyma. The multiple concentric layers (MCLs) technique was trained and tested using the craniocaudal views of 270 mammographic cases with biopsy proven malignant masses from the digital database of screening mammography. One-half of the available cases were used for optimizing the parameters of the detection algorithm. The remaining cases were used for testing. During testing, malignant masses were detected with 92%, 88%, and 81% sensitivity at 5.4, 2.4, and 0.6 false positive marks per image. Testing on 82 normal screening mammograms showed a false positive rate of 5.0, 1.7, and 0.2 marks per image at the previously reported operating points. Furthermore, additional evaluation on 135 benign cases produced a significantly lower detection rate for benign masses (61.6%, 58.3%, and 43.7% at 5.1, 2.8, and 1.2 false positives per image, respectively). Overall, MCL is a promising computer-assisted detection strategy for screening mammograms to identify malignant masses while maintaining the detection rate of benign masses considerably lower.

Fuzzy entropy based detection of suspicious masses in digital mammogram images

Mammography is the standard method for screening and detecting breast abnormalities. In this paper, we propose a novel scheme for suspicious lesion detection in digital mammograms. The proposed scheme is based on image thresholding. The optimal threshold is determined by minimizing the fuzzy entropy of the image. Moreover, the paper introduces a new block-based performance criterion to compare between the computer generated and the radiologist segmented images. Experimental results over a set of sample images showed that the proposed scheme produces accurate segmentation results when compared with the manual results produced by radiologists. Hence the proposed scheme can be used as an effective tool in monitoring and detecting suspicious lesions on digital mammogram images.

Computerized detection of breast masses in digitized mammograms

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.

Detection of simulated nodules on clinical radiographs: dose reduction at digital posteroanterior chest radiography

PURPOSE

To determine to what extent dose reduction results in decreased detection of simulated nodules on patient digital posteroanterior (PA) chest radiographs.

MATERIALS AND METHODS

Raw data from 20 clinical digital PA chest images that were reported as having normal findings and that were obtained with a slot-scan charge-coupled device system were used. For research protocol that concerns data with patient identities concealed, institutional review board approval is not required. One hundred twenty nodules varying in size and signal intensity were digitally simulated and added to the chest images. Hard copies were printed to represent a 100% dose and, by adding noise, to represent simulated patient doses of 50%, 25%, and 12%. Four radiologists reviewed images. Each lesion was registered as "detected" or "not detected." A semiparametric logistic regression model was used for statistical analysis.

RESULTS

The decrease in radiation dose from 100% to 50%, 25%, or 12% had no effect on lesion detection in the lungs. The decrease in radiation dose had an effect on lesion detection in the mediastinum, as probabilities deteriorated from the 100% dose to the 50%, 25%, and 12% dose with each step. Probabilities of smaller detection rates when compared with that of the reference category (100% dose) were 0.97 (95% confidence interval [CI]: -0.86, 0.012) for the 50% dose, 1 (CI: -0.59, -0.61) for the 25% dose, and 1 (CI: -2.41, -1.22) for the 12% dose. CIs for the effects were on the log(odds). Detection probability decreased with smaller and lower signal intensity lesions.

CONCLUSION

At clinical digital radiography, dose reduction resulted in decreased observer detection of simulated nodules in the mediastinum but not in the lungs.

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.

Computerized mass detection for digital breast tomosynthesis directly from the projection images

Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number.of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

A study on the computerized fractal analysis of architectural distortion in screening mammograms

Architectural distortion (AD) is a sign of malignancy often missed during mammographic interpretation. The purpose of this study was to explore the application of fractal analysis to the investigation of AD in screening mammograms. The study was performed using mammograms from the Digital Database for Screening Mammography (DDSM). The fractal dimension (FD) of mammographic regions of interest (ROIs) was calculated using the circular average power spectrum technique. Initially, the variability of the FD estimates depending on ROI location, mammographic view and breast side was studied on normal mammograms. Then, the estimated FD was evaluated using receiver operating characteristics (ROC) analysis to determine if it can discriminate ROIs depicting AD from those depicting normal breast parenchyma. The effect of several factors such as ROI size, image subsampling and breast density was studied in detail. Overall, the average FD of the normal ROIs was statistically significantly higher than that of the ROIs with AD. This result was consistent across all factors studied. For the studied set of implementation parameters, the best ROC performance achieved was 0.89 +/- 0.02. The generalizability of these conclusions across different digitizers was also demonstrated.

Interval change analysis to improve computer aided detection in mammography

We are developing computer aided diagnosis (CAD) techniques to study interval changes between two consecutive mammographic screening rounds. We have previously developed methods for the detection of malignant masses based on features extracted from single mammographic views. The goal of the present work was to improve our detection method by including temporal information in the CAD program. Toward this goal, we have developed a regional registration technique. This technique links a suspicious location on the current mammogram with a corresponding location on the prior mammogram. The novelty of our method is that the search for correspondence is done in feature space. This has the advantage that very small lesions and architectural distortions may be found as well. Following the linking process several features are calculated for the current and prior region. Temporal features are obtained by combining the feature values from both regions. We evaluated the detection performance with and without the use of temporal features on a data set containing 2873 temporal film pairs from 938 patients. There were 589 cases in which the current mammogram contained exactly one malignant mass. Cross validation was used to partition the data set into a train set and a test set. The train set was used for feature selection and classifier training, the test set for classifier evaluation. FROC (free response operating characteristic) analysis showed an improvement in detection performance with the use of temporal features.

Use of border information in the classification of mammographic masses

We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (A(z)) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.

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

In this paper we develop an automatic regional registration method to find corresponding masses on prior and current mammograms. The method contains three steps. In the first, we globally align both images. Then, for each mass lesion on the current view, we define a search area on the prior view, which is likely to contain the same mass lesion. Third, at each location in this search area we calculate a registration measure to quantify how well this location matches the mass lesion on the current view. Finally we select the best location. To determine the performance of our method we compare it to several other registration methods. On a dataset of 389 temporal mass pairs our method correctly links 82% of prior and current mass lesions, whereas other methods achieve at most 72%.

An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.

Improved image contrast of calcifications in breast tissue specimens using diffraction enhanced imaging

The contrast of calcifications in images of breast tissue specimens using a synchrotron-based diffraction enhanced imaging (DEI) apparatus has been measured and is compared to the contrast in images acquired using a conventional synchrotron-based radiographic imaging modality. DEI is an imaging modality which derives image contrast from x-ray absorption, refraction and small-angle scatter-rejection (extinction), unlike conventional radiographic techniques, which can only derive contrast from absorption. DEI is accomplished by inserting an analyser crystal in the beam path between the sample and the detector. Two of the three breast tissue specimens contained calcifications associated with cancer, while a third contained benign calcifications. Results of the image analysis indicate that the DEI contrast of images taken with the analyser crystal tuned to the peak of its rocking curve, was as much as 19 times that of the conventional radiograph, with an average of 5.5 for all calcifications. This improved image contrast for even near-pixel-size calcifications suggests potential utility for DEI in breast imaging.

Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

Automatic evaluation of the image quality of a mammographic phantom

In this work a method has been developed to analyse the digital image quality of a mammographic phantom by means of automatic process techniques. The techniques used for the digital image treatment are standard techniques as the image thresholding to detect objects, the regional growing for pixels pooling and the morphological operator application to determine the objects shape and size, etc. This study allows the obtention of information about the phantom characteristics, that due to its small size and lowly contrast can be obtained very difficultly by direct observation. The final aim of this work is to obtain one or more parameters to characterize the reference phantom quality image in an objective way. These parameters will serve to compare images obtained at different mammographic centers and also, to study the temporal evolution of the image quality produced by determined mammographic equipment.

Learning contextual relationships in mammograms using a hierarchical pyramid neural network

This paper describes a pattern recognition architecture, which we term hierarchical pyramid/neural network (HPNN), that learns to exploit image structure at multiple resolutions for detecting clinically significant features in digital/digitized mammograms. The HPNN architecture consists of a hierarchy of neural networks, each network receiving feature inputs at a given scale as well as features constructed by networks lower in the hierarchy. Networks are trained using a novel error function for the supervised learning of image search/detection tasks when the position of the objects to be found is uncertain or ill defined. We have evaluated the HPNN's ability to eliminate false positive (FP) regions of interest generated by the University of Chicago's (UofC) Computer-aided diagnosis (CAD) systems for microcalcification and mass detection. Results show that the HPNN architecture, trained using the uncertain object position (UOP) error function, reduces the FP rate of a mammographic CAD system by approximately 50% without significant loss in sensitivity. Investigation into the types of FPs that the HPNN eliminates suggests that the pattern recognizer is automatically learning and exploiting contextual information. Clinical utility is demonstrated through the evaluation of an integrated system in a clinical reader study. We conclude that the HPNN architecture learns contextual relationships between features at multiple scales and integrates these features for detecting microcalcifications and breast masses.

Wavelets in temporal and spatial processing of biomedical images

We review some of the most recent advances in the area of wavelet applications in medical imaging. We first review key concepts in the processing of medical images with wavelet transforms and multiscale analysis, including time-frequency tiling, overcomplete representations, higher dimensional bases, symmetry, boundary effects, translational invariance, orientation selectivity, and best-basis selection. We next describe some applications in magnetic resonance imaging, including activation detection and denoising of functional magnetic resonance imaging and encoding schemes. We then present an overview in the area of ultrasound, including computational anatomy with three-dimensional cardiac ultrasound. Next, wavelets in tomography are reviewed, including their relationship to the radon transform and applications in position emission tomography imaging. Finally, wavelet applications in digital mammography are reviewed, including computer-assisted diagnostic systems that support the detection and classification of small masses and methods of contrast enhancement.

Detection of breast masses in mammograms by density slicing and texture flow-field analysis

We propose a method for the detection of masses in mammographic images that employs Gaussian smoothing and sub-sampling operations as preprocessing steps. The mass portions are segmented by establishing intensity links from the central portions of masses into the surrounding areas. We introduce methods for analyzing oriented flow-like textural information in mammograms. Features based on flow orientation in adaptive ribbons of pixels across the margins of masses are proposed to classify the regions detected as true mass regions or false-positives (FPs). The methods yielded a mass versus normal tissue classification accuracy represented as an area (Az) of 0.87 under the receiver operating characteristics (ROCs) curve with a dataset of 56 images including 30 benign disease, 13 malignant disease, and 13 normal cases selected from the mini Mammographic Image Analysis Society database. A sensitivity of 81% was achieved at 2.2 FPs/image. Malignant tumor versus normal tissue classification resulted in a higher Az value of 0.9 under the ROC curve using only the 13 malignant and 13 normal cases with a sensitivity of 85% at 2.45 FPs/image. The mass detection algorithm could detect all the 13 malignant tumors successfully, but achieved a success rate of only 63% (19/30) in detecting the benign masses. The mass regions that were successfully segmented were further classified as benign or malignant disease by computing five texture features based on gray-level co-occurrence matrices (GCMs) and using the features in a logistic regression method. The features were computed using adaptive ribbons of pixels across the boundaries of the masses. Benign versus malignant classification using the GCM-based texture features resulted in Az = 0.79 with 19 benign and 13 malignant cases.

An automatic method to discriminate malignant masses from normal tissue in digital mammograms

Specificity levels of automatic mass detection methods in mammography are generally rather low, because suspicious looking normal tissue is often hard to discriminate from real malignant masses. In this work a number of features were defined that are related to image characteristics that radiologists use to discriminate real lesions from normal tissue. An artificial neural network was used to map the computed features to a measure of suspiciousness for each region that was found suspicious by a mass detection method. Two data sets were used to test the method. The first set of 72 malignant cases (132 films) was a consecutive series taken from the Nijmegen screening programme, 208 normal films were added to improve the estimation of the specificity of the method. The second set was part of the new DDSM data set from the University of South Florida. A total of 193 cases (772 films) with 372 annotated malignancies was used. The measure of suspiciousness that was computed using the image characteristics was successful in discriminating tumours from false positive detections. Approximately 75% of all cancers were detected in at least one view at a specificity level of 0.1 false positive per image.