Yi Ta Wu

Association of Computerized Mammographic Parenchymal Pattern Measure with Breast Cancer Risk: A Pilot Case-Control Study

PURPOSE To develop a computerized mammographic parenchymal pattern (MPP) measure and investigate its association with breast cancer risk. MATERIALS AND METHODS A pilot case-control study was conducted by collecting mammograms from 382 subjects retrospectively. The study was institutional review board approved and HIPAA compliant. Informed consent was waived. The cases included the contralateral mammograms of cancer patients (n = 136) obtained at least 1 year before diagnosis. The controls included mammograms of healthy subjects (n = 246) who had cancer-free follow-up for at least 5 years. The data set was historically divided into a training set and an independent test set. An MPP measure was designed to analyze the texture patterns of fibroglandular tissue in the retroareolar region. Odds ratios (ORs) were used to assess the association between breast cancer risk and MPP. To test the trend in ORs, we divided the MPP measure into three categories (C1, C2, and C3) on the basis of its values from low to high, with C1 as the baseline. The confounding factors in this study included patient age, body mass index, first-degree relatives with history of breast cancer, number of previous breast biopsies, and percentage density (PD). RESULTS Among all of the subjects from the training and test data sets, the Pearson product-moment correlation coefficient between MPP and PD was 0.13. With logistic regression to adjust the confounding, the adjusted ORs for C2 and C3 relative to C1 in the test set were 2.82 (P = .041) and 13.89 (P < .001), respectively. CONCLUSION The proposed MPP measure demonstrated a strong association with breast cancer risk and has the potential to serve as an independent factor for risk prediction.

Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching.

An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three-dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross-correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the radiologist. The average Euclidean distance error was 2.7 +/- 3.3 mm. Only two pairs had an error larger than 10 mm. The average volume overlap measure was 0.71 +/- 0.24. Eighty-three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was 93/101.

Application of boundary detection information in breast tomosynthesis reconstruction.

Digital tomosynthesis mammography (DTM) is one of the most promising techniques that can potentially improve early detection of breast cancers. DTM can provide three-dimensional (3D) structural information by reconstructing the whole imaged volume from a sequence of projection-view (PV) mammograms that are acquired at a small number of projection angles over a limited angular range. Our previous study showed that simultaneous algebraic reconstruction technique (SART) can produce satisfactory tomosynthesized image quality compared to maximum likelihood-type algorithms. To improve the efficiency of DTM reconstruction and address the problem of boundary artifacts, we have developed methods to incorporate both two-dimensional (2D) and 3D breast boundary information within the SART reconstruction algorithm in this study. A second generation GE prototype tomosynthesis mammography system with a stationary digital detector was used for PV image acquisition from 21 angles in 3 degrees increments over a +/- 30 degrees angular range. The 2D breast boundary curves on all PV images were obtained by automated segmentation and were used to restrict the SART reconstruction to be performed only within the breast volume. The computation time of SART reconstruction was reduced by 76.3% and 69.9% for cranio-caudal and mediolateral oblique views, respectively, for the chosen example. In addition, a 3D conical trimming method was developed in which the 2D breast boundary curves from all PVs were back projected to generate the 3D breast surface. This 3D surface was then used to eliminate the multiple breast shadows outside the breast volume due to reconstruction by setting these voxels to a constant background value. Our study demonstrates that, by using the 2D and 3D breast boundary information, all breast boundary and most detector boundary artifacts can be effectively removed on all tomosynthesized slices.

Characterization of masses in digital breast tomosynthesis: Comparison of machine learning in projection views and reconstructed slices

PURPOSE In digital breast tomosynthesis (DBT), quasi-three-dimensional (3D) structural information is reconstructed from a small number of 2D projection view (PV) mammograms acquired over a limited angular range. The authors developed preliminary computer-aided diagnosis (CADx) methods for classification of malignant and benign masses and compared the effectiveness of analyzing lesion characteristics in the reconstructed DBT slices and in the PVs. METHODS A data set of MLO view DBT of 99 patients containing 107 masses (56 malignant and 51 benign) was collected at the Massachusetts General Hospital with IRB approval. The DBTs were obtained with a GE prototype system which acquired 11 PVs over a 50 degree arc. The authors reconstructed the DBTs at 1 mm slice interval using a simultaneous algebraic reconstruction technique. The region of interest (ROI) containing the mass was marked by a radiologist in the DBT volume and the corresponding ROIs on the PVs were derived based on the imaging geometry. The subsequent processes were fully automated. For classification of masses using the DBT-slice approach, the mass on each slice was segmented by an active contour model initialized with adaptive k-means clustering. A spiculation likelihood map was generated by analysis of the gradient directions around the mass margin and spiculation features were extracted from the map. The rubber band straightening transform (RBST) was applied to a band of pixels around the segmented mass boundary. The RBST image was enhanced by Sobel filtering in the horizontal and vertical directions, from which run-length statistics texture features were extracted. Morphological features including those from the normalized radial length were designed to describe the mass shape. A feature space composed of the spiculation features, texture features, and morphological features extracted from the central slice alone and seven feature spaces obtained by averaging the corresponding features from three to 19 slices centered at the central slice were compared. For classification of masses using the PV approach, a feature extraction process similar to that described above for the DBT approach was performed on the ROIs from the individual PVs. Six feature spaces obtained from the central PV alone and by averaging the corresponding features from three to 11 PVs were formed. In each feature space for either the DBT-slice or the PV approach, a linear discriminant analysis classifier with stepwise feature selection was trained and tested using a two-loop leave-one-case-out resampling procedure. Simplex optimization was used to guide feature selection automatically within the training set in each leave-one-case-out cycle. The performance of the classifiers was evaluated by the area (Az) under the receiver operating characteristic curve. RESULTS The test Az values from the DBT-slice approach ranged from 0.87 +/- 0.03 to 0.93 +/- 0.02, while those from the PV approach ranged from 0.78 +/- 0.04 to 0.84 +/- 0.04. The highest test Az of 0.93 +/- 0.02 from the nine-DBT-slice feature space was significantly (p = 0.006) better than the highest test Az of 0.84 +/- 0.04 from the nine-PV feature space. CONCLUSION The features of breast lesions extracted from the DBT slices consistently provided higher classification accuracy than those extracted from the PV images.

Automated detection of breast vascular calcification on full-field digital mammograms

Breast vascular calcifications (BVCs) are calcifications that line the blood vessel walls in the breast and appear as parallel or tubular tracks on mammograms. BVC is one of the major causes of the false positive (FP) marks from computer-aided detection (CADe) systems for screening mammography. With the detection of BVCs and the calcified vessels identified, these FP clusters can be excluded. Moreover, recent studies reported the increasing interests in the correlation between mammographically visible BVCs and the risk of coronary artery diseases. In this study, we developed an automated BVC detection method based on microcalcification prescreening and a new k-segments clustering algorithm. The mammogram is first processed with a difference-image filtering technique designed to enhance calcifications. The calcification candidates are selected by an iterative process that combines global thresholding and local thresholding. A new k-segments clustering algorithm is then used to find a set of line segments that may be caused by the presence of calcified vessels. A linear discriminant analysis (LDA) classifier was designed to reduce false segments that are not associated with BVCs. Four features for each segment selected with stepwise feature selection were used for this LDA classification. Finally, the neighboring segments were linked and dilated with morphological dilation to cover the regions of calcified vessels. A data set of 16 FFDM cases with vascular calcifications was collected for this preliminary study. Our preliminary result demonstrated that breast vascular calcifications can be accurately detected and the calcified vessels identified. It was found that the automated method can achieve a detection sensitivity of 65%, 70%, and 75% at 6.1 mm, 8.4 mm, and 12.6mm FP segments/image, respectively, without any true clustered microcalcifications being falsely marked. Further work is underway to improve this method and to incorporate it into our FFDM CADe system.

Truncation artifact and boundary artifact reduction in breast tomosynthesis reconstruction

Digital Tomosynthesis Mammography (DTM) is an emerging technique that has the potential to improve breast cancer detection. DTM acquires low-dose mammograms at a number of projection angles over a limited angular range and reconstructs the 3D breast volume. Due to the limited number of projections within a limited angular range and the finite size of the detector, DTM reconstruction contains boundary and truncation artifacts that degrade the image quality of the tomosynthesized slices, especially that of the boundary and truncated regions. In this work, we developed artifact reduction methods that make use of both 2D and 3D breast boundary information and local intensity-equalization and tissue-compensation techniques. A breast phantom containing test objects and a selected DTM patient case were used to evaluate the effects of artifact reduction. The contrast-to-noise ratio (CNR), the normalized profiles of test objects, and a non-uniformity error index were used as performance measures. A GE prototype DTM system was used to acquire 21 PVs in 3° increments over a ±30° angular range. The Simultaneous Algebraic Reconstruction Technique (SART) was used for DTM reconstruction. Our results demonstrated that the proposed methods can improve the image quality both qualitatively and quantitatively, resulting in increased CNR value, background uniformity and an overall reconstruction quality comparable to that without truncation. For the selected DTM patient case, the obscured breast structural information near the truncated regions was essentially recovered. In addition, restricting SART reconstruction to be performed within the estimated 3D breast volume increased the computation efficiency.

A dynamic multiple thresholding method for automated breast boundary detection in digitized mammograms

We have previously developed a breast boundary detection method by using a gradient-based method to search for the breast boundary (GBB). In this study, we developed a new dynamic multiple thresholding based breast boundary detection system (MTBB). The initial breast boundary (MTBB-Initial) is obtained based on the analysis of multiple thresholds on the image. The final breast boundary (MTBB-Final) is obtained based on the initial breast boundary and the gradient information from horizontal and the vertical Sobel filtering. In this way, it is possible to accurately segment the breast area from the background region. The accuracy of the breast boundary detection algorithm was evaluated by comparison with an experienced radiologists manual segmentation using three performance metrics: the Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap (AOM). It was found that 68%, 85%, and 90% of images have HDist errors less than 6 mm for GBB, MTBB-Initial, and MTBB-Final, respectively. Ninety-five percent, 96%, and 97% of the images have AMinDist errors less than 1.5 mm for GBB, MTBB-Initial, and MTBB-Final, respectively. Ninety-six percent, 97%, and 99% of the images have AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. It was found that the performance of the proposed method was improved in comparison to our previous method.

Breast mass segmentation on dynamic contrast-enhanced magnetic resonance scans using the level set method

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans that were performed to monitor breast cancer response to neoadjuvant chemotherapy. A radiologist experienced in interpreting breast MR scans defined the mass using a cuboid volume of interest (VOI). Our method then used the K-means clustering algorithm followed by morphological operations for initial mass segmentation on the VOI. The initial segmentation was then refined by a three-dimensional level set (LS) method. The velocity field of the LS method was formulated in terms of the mean curvature which guaranteed the smoothness of the surface and the Sobel edge information which attracted the zero LS to the desired mass margin. We also designed a method to reduce segmentation leak by adapting a region growing technique. Our method was evaluated on twenty DCE-MR scans of ten patients who underwent neoadjuvant chemotherapy. Each patient had pre- and post-chemotherapy DCE-MR scans on a 1.5 Tesla magnet. Computer segmentation was applied to coronal T1-weighted images. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.0 mm. The flip angle was 15 degrees, repetition time ranged from 5.98 to 6.7 ms, and echo time ranged from 1.2 to 1.3 ms. The computer segmentation results were compared to the radiologists manual segmentation in terms of the overlap measure defined as the ratio of the intersection of the computer and the radiologists segmentations to the radiologists segmentation. Pre- and post-chemotherapy masses had overlap measures of 0.81±0.11 (mean±s.d.) and 0.70±0.21, respectively.

Comparison of Mammographic Parenchymal Patterns of Normal Subjects and Breast Cancer Patients

In this study, we compared the texture features of mammographic parenchymal patterns (MPPs) of normal subjects and breast cancer patients and evaluated whether a texture classifier can differentiate their MPPs. The breast image was first segmented from the surrounding image background by boundary detection. Regions of interest (ROIs) were extracted from the segmented breast area in the retroareolar region on the cranio-caudal (CC) view mammograms. A mass set (MS) of ROIs was extracted from the mammograms with cancer, but ROIs overlapping with the mass were excluded. A contralateral set (CS) of ROIs was extracted from the contralateral mammograms. A normal set (NS) of ROIs was extracted from one CC view mammogram of the normal subjects. Each data set was randomly separated into two independent subsets for 2-fold cross-validation training and testing. Texture features from run-length statistics (RLS) and newly developed region-size statistics (RSS) were extracted to characterize the MPP of the breast. Linear discriminant analysis (LDA) was performed to compare the MPP difference in each of the three pairs: MS-vs-NS, CS-vs-NS, and MS-vs-CS. The Az values for the three pairs were 0.79, 0.73, and 0.56, respectively. These results indicate that the MPPs of the contralateral breast of breast cancer patients exhibit textures comparable to that of the affected breast and that the MPPs of cancer patients are different from those of normal subjects.

Digital tomosynthesis mammography : comparison of mass classification using 3D slices and 2D projection views

We are developing computer-aided diagnosis (CADx) methods for classification of masses on digital breast tomosynthesis mammograms (DBTs). A DBT data set containing 107 masses (56 malignant and 51 benign) collected at the Massachusetts General Hospital was used. The DBTs were obtained with a GE prototype system which acquired 11 projection views (PVs) over a 50-degree arc. We reconstructed the DBTs at 1-mm slice interval using a simultaneous algebraic reconstruction technique. The regions of interest (ROIs) containing the masses in the DBT volume and the corresponding ROIs on the PVs were identified. The mass on each slice or each PV was segmented by an active contour model. Spiculation measures, texture features, and morphological features were extracted from the segmented mass. Four feature spaces were formed: (1) features from the central DBT slice, (2) average features from 5 DBT slices centered at the central slice, (3) features from the central PV, and (4) average features from all 11 PVs. In each feature space, a linear discriminant analysis classifier with stepwise feature selection was trained and tested using a two loop leave-one-case-out procedure. The test Az of 0.91±0.03 from the 5-DBT-slice feature space was significantly (p=0.003) higher than that of 0.84±0.04 from the 1-DBT-slice feature space. The test Az of 0.83±0.04 from the 11-PV feature space was not significantly different (p=0.18) from that of 0.79±0.04 from the 1-PV feature space. The classification accuracy in the 5-DBT-slice feature space was significantly better (p=0.006) than that in the 11-PV feature space. The results demonstrate that the features of breast lesions extracted from the DBT slices may provide higher classification accuracy than those from the PV images.