Chintana Paramagul

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76/spl plusmn/0.13,0.74 /spl plusmn/0.11, and 0.74/spl plusmn/0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area A/sub z/ under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computers Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computers Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Computerized nipple identification for multiple image analysis in computer-aided diagnosis

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations which were used as the gold standard. In the training data set, 301 nipples were positively identified and were referred to as visible nipples. Seventy six nipples could not be positively identified and were referred to as invisible nipples. The radiologists provided their estimation of the nipple locations in the latter group for comparison with the computer estimates. The computerized method could detect 89.37% (269/301) of the visible nipples and 69.74% (53/76) of the invisible nipples within 1 cm of the gold standard. In the test data set, 298 and 69 of the nipples were classified as visible and invisible, respectively. 92.28% (275/298) of the visible nipples and 53.62% (37/69) of the invisible nipples were identified within 1 cm of the gold standard. The results demonstrate that the nipple locations on digitized mammograms can be accurately detected if they are visible and can be reasonably estimated if they are invisible. Automated nipple detection will be an important step towards multiple image analysis for CAD.

Is lobular carcinoma in situ as a component of breast carcinoma a risk factor for local failure after breast-conserving therapy? Results of a matched pair analysis.

The goals of the current study were to compare the clinicopathologic presentations of patients with lobular carcinoma in situ (LCIS) as a component of breast carcinoma who were treated with breast conserving surgery (BCS) and radiation therapy (RT) with those of patients without LCIS as part of their primary tumor and to report rates of local control by overall cohort and specifically in patients with positive margins for LCIS and multifocal LCIS.

Is lobular carcinoma in situ as a component of breast carcinoma a risk factor for local failure after breast-conserving therapy? Presented in part at the 46th American Society for Therapeutic Radiology and Oncology Meeting, Atlanta, Georgia, October 3–7, 2004.

The goals of the current study were to compare the clinicopathologic presentations of patients with lobular carcinoma in situ (LCIS) as a component of breast carcinoma who were treated with breast conserving surgery (BCS) and radiation therapy (RT) with those of patients without LCIS as part of their primary tumor and to report rates of local control by overall cohort and specifically in patients with positive margins for LCIS and multifocal LCIS.

Digital Breast Tomosynthesis: Observer Performance of Clustered Microcalcification Detection on Breast Phantom Images Acquired with an Experimental System Using Variable Scan Angles, Angular Increments, and Number of Projection Views

PURPOSE To investigate the dependence of microcalcification cluster detectability on tomographic scan angle, angular increment, and number of projection views acquired at digital breast tomosynthesis ( DBT digital breast tomosynthesis ). MATERIALS AND METHODS A prototype DBT digital breast tomosynthesis system operated in step-and-shoot mode was used to image breast phantoms. Four 5-cm-thick phantoms embedded with 81 simulated microcalcification clusters of three speck sizes (subtle, medium, and obvious) were imaged by using a rhodium target and rhodium filter with 29 kV, 50 mAs, and seven acquisition protocols. Fixed angular increments were used in four protocols (denoted as scan angle, angular increment, and number of projection views, respectively: 16°, 1°, and 17; 24°, 3°, and nine; 30°, 3°, and 11; and 60°, 3°, and 21), and variable increments were used in three (40°, variable, and 13; 40°, variable, and 15; and 60°, variable, and 21). The reconstructed DBT digital breast tomosynthesis images were interpreted by six radiologists who located the microcalcification clusters and rated their conspicuity. RESULTS The mean sensitivity for detection of subtle clusters ranged from 80% (22.5 of 28) to 96% (26.8 of 28) for the seven DBT digital breast tomosynthesis protocols; the highest sensitivity was achieved with the 16°, 1°, and 17 protocol (96%), but the difference was significant only for the 60°, 3°, and 21 protocol (80%, P < .002) and did not reach significance for the other five protocols (P = .01-.15). The mean sensitivity for detection of medium and obvious clusters ranged from 97% (28.2 of 29) to 100% (24 of 24), but the differences fell short of significance (P = .08 to >.99). The conspicuity of subtle and medium clusters with the 16°, 1°, and 17 protocol was rated higher than those with other protocols; the differences were significant for subtle clusters with the 24°, 3°, and nine protocol and for medium clusters with 24°, 3°, and nine; 30°, 3°, and 11; 60°, 3° and 21; and 60°, variable, and 21 protocols (P < .002). CONCLUSION With imaging that did not include x-ray source motion or patient motion during acquisition of the projection views, narrow-angle DBT digital breast tomosynthesis provided higher sensitivity and conspicuity than wide-angle DBT digital breast tomosynthesis for subtle microcalcification clusters.

Digital breast tomosynthesis: studies of the effects of acquisition geometry on contrast-to-noise ratio and observer preference of low-contrast objects in breast phantom images

The effect of acquisition geometry in digital breast tomosynthesis was evaluated with studies of contrast-to-noise ratios (CNRs) and observer preference. Contrast-detail (CD) test objects in 5 cm thick phantoms with breast-like backgrounds were imaged. Twelve different angular acquisitions (average glandular dose for each ~1.1 mGy) were performed ranging from narrow angle 16° with 17 projection views (16d17p) to wide angle 64d17p. Focal slices of SART-reconstructed images of the CD arrays were selected for CNR computations and the reader preference study. For the latter, pairs of images obtained with different acquisition geometries were randomized and scored by 7 trained readers. The total scores for all images and readings for each acquisition geometry were compared as were the CNRs. In general, readers preferred images acquired with wide angle as opposed to narrow angle geometries. The mean percent preferred was highly correlated with tomosynthesis angle (R = 0.91). The highest scoring geometries were 60d21p (95%), 64d17p (80%), and 48d17p (72%); the lowest scoring were 16d17p (4%), 24d9p (17%) and 24d13p (33%). The measured CNRs for the various acquisitions showed much overlap but were overall highest for wide-angle acquisitions. Finally, the mean reader scores were well correlated with the mean CNRs (R = 0.83).

Suspicious Breast Lesions: Assessment of 3D Doppler US Indexes for Classification in a Test Population and Fourfold Cross-Validation Scheme

PURPOSE To assess the diagnostic performance of various Doppler ultrasonographic (US) vascularity measures in conjunction with grayscale (GS) criteria in differentiating benign from malignant breast masses, by using histologic findings as the reference standard. MATERIALS AND METHODS Institutional Review Board and HIPAA standards were followed. Seventy-eight women (average age, 49 years; range, 26-70 years) scheduled for breast biopsy were included. Thirty-eight patient scans were partially analyzed and published previously, and 40 additional scans were used as a test set to evaluate previously determined classification indexes. In each patient, a series of color Doppler images was acquired and reconstructed into a volume encompassing a suspicious mass, identified by a radiologist-defined ellipsoid, in which six Doppler vascularity measures were calculated. Radiologist GS ratings and patient age were also recorded. Multivariable discrimination indexes derived from the learning set were applied blindly to the test set. Overall performance was also confirmed by using a fourfold cross-validation scheme on the entire population. RESULTS By using all cases (46 benign, 32 malignant), the area under the receiver operating characteristic curve (A(z)) values confirmed results of previous analyses: Speed-weighted pixel density (SWPD) performed the best as a diagnostic index, although statistical significance (P = .01) was demonstrated only with respect to the normalized power-weighted pixel density. In both learning and test sets, the three-variable index (SWPD-age-GS) displayed significantly better diagnostic performance (A(z) = 0.97) than did any single index or the one two-variable index (age-GS) that could be obtained without the data from the Doppler scan. Results of the cross validation confirmed the trends in the two data sets. CONCLUSION Quantitative Doppler US vascularity measurements considerably contribute to malignant breast tissue identification beyond subjective GS evaluation alone. The SWPD-age-GS index has high performance (A(z) = 0.97), regardless of incidental performance variations in its single variable components.

Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans and to evaluate its potential for estimating tumor volume on pre- and postchemotherapy images and tumor change in response to treatment. A radiologist experienced in interpreting breast MR scans defined a cuboid volume of interest (VOI) enclosing the mass in the MR volume at one time point within the sequence of DCE-MR scans. The corresponding VOIs over the entire time sequence were then automatically extracted. A new 3D VOI representing the local pharmacokinetic activities in the VOI was generated from the 4D VOI sequence by summarizing the temporal intensity enhancement curve of each voxel with its standard deviation. The method then used the fuzzy c-means (FCM) clustering algorithm followed by morphological filtering for initial mass segmentation. The initial segmentation was refined by the 3D 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, the Sobel edge information which attracted the zero LS to the desired mass margin, and the FCM membership function which improved segmentation accuracy. The method was evaluated on 50 DCE-MR scans of 25 patients who underwent neoadjuvant chemotherapy. Each patient had pre- and postchemotherapy DCE-MR scans on a 1.5 T magnet. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.5 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. Computer segmentation was applied to the coronal T1-weighted images. For comparison, the same radiologist who marked the VOI also manually segmented the mass on each slice. The performance of the automated method was quantified using an overlap measure, defined as the ratio of the intersection of the computer and the manual segmentation volumes to the manual segmentation volume. Pre- and postchemotherapy masses had overlap measures of 0.81 +/- 0.13 (mean +/- s.d.) and 0.71 +/- 0.22, respectively. The percentage volume reduction (PVR) estimated by computer and the radiologist were 55.5 +/- 43.0% (mean +/- s.d.) and 57.8 +/- 51.3%, respectively. Paired Students t test indicated that the difference between the mean PVRs estimated by computer and the radiologist did not reach statistical significance (p = 0.641). The automated mass segmentation method may have the potential to assist physicians in monitoring volume change in breast masses in response to treatment.

Computerized image analysis: Texture‐field orientation method for pectoral muscle identification on MLO‐view mammograms

PURPOSE To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms. METHODS The authors designed a gradient-based directional kernel (GDK) filter to enhance the linear texture structures, and a gradient-based texture analysis to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GDK filter for ridge point extraction. The extracted ridge points were validated and the ridges that were less likely to lie on the pectoral boundary were removed automatically. A shortest-path finding method was used to generate a probability image that represented the likelihood that each remaining ridge point lay on the true pectoral boundary. Finally, the pectoral boundary was tracked by searching for the ridge points with the highest probability lying on the pectoral boundary. A data set of 130 MLO-view digitized film mammograms (DFMs) from 65 patients was used to train the TFO algorithm. An independent data set of 637 MLO-view DFMs from 562 patients was used to evaluate its performance. Another independent data set of 92 MLO-view full field digital mammograms (FFDMs) from 92 patients was used to assess the adaptability of the TFO algorithm to FFDMs. The pectoral boundary detection accuracy of the TFO method was quantified by comparison with an experienced radiologists manually drawn pectoral boundary using three performance metrics: The percent overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist). RESULTS The mean and standard deviation of POA, Hdist, and AvgDist were 95.0 +/- 3.6%, 3.45 +/- 2.16 mm, and 1.12 +/- 0.82 mm, respectively. For the POA measure, 91.5%, 97.3%, and 98.9% of the computer detected pectoral muscles had POA larger than 90%, 85%, and 80%, respectively. For the distance measures, 85.4% and 98.0% of the computer detected pectoral boundaries had Hdist within 5 and 10 mm, respectively, and 99.4% of computer detected pectoral muscle boundaries had AvgDist within 5 mm from the radiologists manually drawn boundaries. CONCLUSIONS The pectoral muscle on DFMs can be detected accurately by the automated TFO method. The preliminary study of applying the same pectoral muscle identification algorithm to FFDMs without retraining demonstrates that the TFO method is reasonably robust against the differences in the image properties between the digitized and digital mammograms.