Temporal Pairs Of Mammograms Research Articles

Temporal mammogram image registration using optimized curvilinear coordinates

Registration of mammograms plays an important role in breast cancer computer-aided diagnosis systems. Radiologists usually compare mammogram images in order to detect abnormalities. The comparison of mammograms requires a registration between them. A temporal mammogram registration method is proposed in this paper. It is based on the curvilinear coordinates, which are utilized to cope both with global and local deformations in the breast area. Temporal mammogram pairs are used to validate the proposed method. After registration, the similarity between the mammograms is maximized, and the distance between manually defined landmarks is decreased. In addition, a thorough comparison with the state-of-the-art mammogram registration methods is performed to show its effectiveness.

Shape localization, quantification and correspondence using Region Matching Algorithm

We propose a method for local, region-based matching of planar shapes, especially as those shapes that change over time. This is a problem fundamental to medical imaging, specifically the comparison over time of mammograms. The method is based on the non-emergence and non-enhancement of maxima, as well as the causality principle of integral invariant scale space. The core idea of our Region Matching Algorithm (RMA) is to divide a shape into a number of “salient” regions and then to compare all such regions for local similarity in order to quantitatively identify new growths or partial/complete occlusions. The algorithm has several advantages over commonly used methods for shape comparison of segmented regions. First, it provides improved key-point alignment for optimal shape correspondence. Second, it identifies localized changes such as new growths as well as complete/partial occlusion in corresponding regions by dividing the segmented region into sub-regions based upon the extrema that persist over a sufficient range of scales. Third, the algorithm does not depend upon the spatial locations of mammographic features and eliminates the need for registration to identify salient changes over time. Finally, the algorithm is fast to compute and requires no human intervention. We apply the method to temporal pairs of mammograms in order to detect potentially important differences between them.

Incorporation of fuzzy spatial relation in temporal mammogram registration

This paper presents a method for incorporating fuzzy sets based spatial relations in registering temporal mammogram pairs. In the proposed method, four spatial relations, to the right of, to the left of, below, and above, are considered. The histogram of all possible angles between all pairs of points in a pair of regions of interest (ROIs) is treated as a fuzzy set and spatial relations between the pair of ROIs are characterized by measuring to what degree this fuzzy set approaches the four spatial relations. Based on the spatial relations, association of ROIs of temporal mammogram pairs is then treated as a graph matching problem and registration of temporal mammogram pairs is realized by finding the common subgraph between two graphs representing a pair of temporal mammograms. 95 pairs of real temporal mammograms are used to test the proposed method. 70.8% of matched ROI pairs are visually identified as “good” matches. When the registration results are incorporated in a cancer detection scheme, the Az score (area under the ROC curve) is improved from 0.846 to 0.852. The results demonstrated that registration of temporal mammograms based on fuzzy spatial relations improves the overall detection efficiency.

Computer aided mass detection in mammography with temporal change analysis

This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the mass detection, 5 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the Az score of overall classification performance increased from 0.8843 to 0.8989 and 0.8863 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.

Use of volumetric features for temporal comparison of mass lesions in full field digital mammograms

Temporal comparison of lesions might improve classification between benign and malignant lesions in full-field digital mammograms (FFDM). The authors compare the use of volumetric features for lesion classification, which are computed from dense tissue thickness maps, to the use of mammographic lesion area. Use of dense tissue thickness maps for lesion characterization is advantageous, since it results in lesion features that are invariant to acquisition parameters. The dataset used in the analysis consisted of 60 temporal mammogram pairs comprising 120 mediolateral oblique or craniocaudal views with a total of 65 lesions, of which 41 were benign and 24 malignant. The authors analyzed the performance of four volumetric features, area, and four other commonly used features obtained from temporal mammogram pairs, current mammograms, and prior mammograms. The authors evaluated the individual performance of all features and of different feature sets. The authors used linear discriminant analysis with leave-one-out cross validation to classify different feature sets. Volumetric features from temporal mammogram pairs achieved the best individual performance, as measured by the area under the receiver operating characteristic curve (Az value). Volume change (Az = 0.88) achieved higher Az value than projected lesion area change (Az = 0.78) in the temporal comparison of lesions. Best performance was achieved with a set that consisted of a set of features extracted from the current exam combined with four volumetric features representing changes with respect to the prior mammogram (Az = 0.90). This was significantly better (p = 0.005) than the performance obtained using features from the current exam only (Az = 0.77). Volumetric features from temporal mammogram pairs combined with features from the single exam significantly improve discrimination of benign and malignant lesions in FFDM mammograms compared to using only single exam features. In the comparison with prior mammograms, use of volumetric change may lead to better performance than use of lesion area change.

A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation

Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) interpolation based on corresponding points on the two breasts, and acquire the mammogram registration image. Performance of the proposed algorithm is evaluated by three criterions. The experimental results show that our method is accurate and closely to the source images.

Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis

A computerized regional registration and characterization system for analysis of microcalcification clusters on serial mammograms is being developed in our laboratory. The system consists of two stages. In the first stage, based on the location of a detected cluster on the current mammogram, a regional registration procedure identifies the local area on the prior that may contain the corresponding cluster. A search program is used to detect cluster candidates within the local area. The detected cluster on the current image is then paired with the cluster candidates on the prior image to form true (TP-TP) or false (TP-FP) pairs. Automatically extracted features were used in a newly designed correspondence classifier to reduce the number of false pairs. In the second stage, a temporal classifier, based on both current and prior information, is used if a cluster has been detected on the prior image, and a current classifier, based on current information alone, is used if no prior cluster has been detected. The data set used in this study consisted of 261 serial pairs containing biopsy-proven calcification clusters. An MQSA radiologist identified the corresponding clusters on the mammograms. On the priors, the radiologist rated the subtlety of 30 clusters (out of the 261 clusters) as 9 or 10 on a scale of 1 (very obvious) to 10 (very subtle). Leave-one-case-out resampling was used for feature selection and classification in both the correspondence and malignant/benign classification schemes. The search program detected 91.2% (238/261) of the clusters on the priors with an average of 0.42 FPs/image. The correspondence classifier identified 86.6% (226/261) of the TP-TP pairs with 20 false matches (0.08 FPs/image) relative to the entire set of 261 image pairs. In the malignant/benign classification stage the temporal classifier achieved a test A(z) of 0.81 for the 246 pairs which contained a detection on the prior. In addition, a classifier was designed by using the clusters on the current mammograms only. It achieved a test A(z) of 0.72 in classifying the clusters as malignant and benign. The difference between the performance of the temporal classifier and the current classifier was statistically significant (p=0.0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy.

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.

A mammographic image analysis method to detect and measure changes in breast density

We present an image analysis method that can detect and measure breast density from digitised mammograms. We present initial results on applying our method to characterise breast changes, in particular, changes due to Hormone Replacement Therapy (HRT). It has been established that long-term use of certain hormone replacement therapies can increase the risk of breast cancer, a fact that encourages the notion that objective measures of tissue density can be an important development in breast cancer image analysis. A set of 59 temporal pairs of mammograms of patients undergoing HRT (two images per patient) were used. The clinician’s assessment of density changes constituted the ground truth for evaluating the proposed quantitative measures of density change. The measures we developed are based on the Standard Mammogram Form (SMF) representation of interesting tissue and their performance (agreement with the expert’s description) is also compared to the “interactive thresholding” method that has been used in the past to characterise mammographic density. The results clearly indicate that present methods for measuring mammographic density fail to characterise temporal changes while the proposed measures have the potential to aid the radiologist in assessing temporal density changes both on a global and a local basis.

A regional registration technique for automated interval change analysis of breast lesions on mammograms.

Analysis of interval change is a useful technique for detection of abnormalities in mammographic interpretation. Interval change analysis is routinely used by radiologists and its importance is well-established in clinical practice. As a first step to develop a computerized method for interval change analysis on mammograms, we are developing an automated regional registration technique to identify corresponding lesions on temporal pairs of mammograms. In this technique, the breast is first segmented from the background on the current and previous mammograms. The breast edges are then aligned using a global alignment procedure based on the mutual information between the breast regions in the two images. Using the nipple location and the breast centroid estimated independently on both mammograms, a polar coordinate system is defined for each image. The polar coordinate of the centroid of a lesion detected on the most recent mammogram is used to obtain an initial estimate of its location on the previous mammogram and to define a fan-shaped search region. A search for a matching structure to the lesion is then performed in the fan-shaped region on the previous mammogram to obtain a final estimate of its location. In this study, a quantitative evaluation of registration accuracy has been performed with a data set of 74 temporal pairs of mammograms and ground-truth correspondence information provided by an experienced radiologist. The most recent mammogram of each temporal pair exhibited a biopsy-proven mass. We have investigated the usefulness of correlation and mutual information as search criteria for determining corresponding regions on mammograms for the biopsy-proven masses. In 85% of the cases (63/74 temporal pairs) the region on the previous mammogram that corresponded to the mass on the current mammogram was correctly identified. The region centroid identified by the registration technique had an average distance of 2.8+/-1.9 mm from the centroid of the radiologist-identified region. These results indicate that our new registration technique may be useful for establishing correspondence between structures on current and previous mammograms. Once such a correspondence is established an interval change analysis could be performed to aid in both detection as well as classification of abnormal breast densities.