The calculation of Cardiothoracic Ratio (CTR) in digital chest radiographs would be useful for cardiac anomaly assessment and heart enlargement related disease indication. The purpose of this study was to develop and evaluate a fully automated scheme for calculation of CTR in digital chest radiographs. Our automated method consisted of three steps, i.e., lung region localization, lung segmentation, and CTR calculation. We manually annotated the lung boundary with 84 points in 100 digital chest radiographs, and calculated an average lung model for the subsequent work. Firstly, in order to localize the lung region, generalized Hough transform was employed to identify the upper, lower, and outer boundaries of lung by use of Sobel gradient information. The average lung model was aligned to the localized lung region to obtain the initial lung outline. Secondly, we separately applied dynamic programming method to detect the upper, lower, outer and inner boundaries of lungs, and then linked the four boundaries to segment the lungs. Based on the identified outer boundaries of left lung and right lung, we corrected the center and the declination of the original radiography. Finally, CTR was calculated as a ratio of the transverse diameter of the heart to the internal diameter of the chest, based on the segmented lungs. The preliminary results on 106 digital chest radiographs showed that the proposed method could obtain accurate segmentation of lung based on subjective observation, and achieved sensitivity of 88.9% (40 of 45 abnormalities), and specificity of 100% (i.e. 61 of 61 normal) for the identification of heart enlargements.
Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential
examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal
CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine
transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines
based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was
performed to align the feature points extracted from the registered images in the second step and the reference images.
Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital.
The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective
observation. The subtraction images of the reference images and the rigid and non-rigid registered images could
effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This
would be useful for the screening of lung cancer in our future study.
Cardiovascular diseases are becoming a leading cause of death all over the world. The cardiac function could be evaluated by global and regional parameters of left ventricle (LV) of the heart. The purpose of this study is to develop and evaluate a fully automated scheme for segmentation of LV in short axis cardiac cine MR images. Our fully automated method consists of three major steps, i.e., LV localization, LV segmentation at end-diastolic phase, and LV segmentation propagation to the other phases. First, the maximum intensity projection image along the time phases of the midventricular slice, located at the center of the image, was calculated to locate the region of interest of LV. Based on the mean intensity of the roughly segmented blood pool in the midventricular slice at each phase, end-diastolic (ED) and end-systolic (ES) phases were determined. Second, the endocardial and epicardial boundaries of LV of each slice at ED phase were synchronously delineated by use of a dual dynamic programming technique. The external costs of the endocardial and epicardial boundaries were defined with the gradient values obtained from the original and enhanced images, respectively. Finally, with the advantages of the continuity of the boundaries of LV across adjacent phases, we propagated the LV segmentation from the ED phase to the other phases by use of dual dynamic programming technique. The preliminary results on 9 clinical cardiac cine MR cases show that the proposed method can obtain accurate segmentation of LV based on subjective evaluation.
Amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance (DCE-MR) images are two important indices for breast cancer risk assessment in the clinical practice. The purpose of this study is to develop and evaluate a fully automated scheme for quantitative analysis of FGT and BPE in DCE-MR images. Our fully automated method consists of three steps, i.e., segmentation of whole breast, fibroglandular tissues, and enhanced fibroglandular tissues. Based on the volume of interest extracted automatically, dynamic programming method was applied in each 2-D slice of a 3-D MR scan to delineate the chest wall and breast skin line for segmenting the whole breast. This step took advantages of the continuity of chest wall and breast skin line across adjacent slices. We then further used fuzzy c-means clustering method with automatic selection of cluster number for segmenting the fibroglandular tissues within the segmented whole breast area. Finally, a statistical method was used to set a threshold based on the estimated noise level for segmenting the enhanced fibroglandular tissues in the subtraction images of pre- and post-contrast MR scans. Based on the segmented whole breast, fibroglandular tissues, and enhanced fibroglandular tissues, FGT and BPE were automatically computed. Preliminary results of technical evaluation and clinical validation showed that our fully automated scheme could obtain good segmentation of the whole breast, fibroglandular tissues, and enhanced fibroglandular tissues to achieve accurate assessment of FGT and BPE for quantitative analysis of breast cancer risk.
Breast segmentation is an important and challenging task for computerized analysis of background parenchymal enhancement (BPE) in dynamic contrast enhanced magnetic resonance images (DCE-MRI). The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of whole breast in three-dimensional (3-D) DCE-MRI. The whole breast segmentation consists of two steps, i.e., the delineation of the chest wall and breast skin line. A sectional dynamic programming method was first designed in each 2-D slice to trace the upper and/or lower boundaries of the chest wall. The statistical distribution of gray levels of the breast skin line was employed as weighting factor to enhance the skin line, and dynamic programming was then applied to delineate breast skin line slice-by-slice within the automatically extracted volume of interest (VOI). Our method also took advantages of the continuity of chest wall and skin line across adjacent slices. Finally, the segmented breast skin line and the detected chest wall were connected to create the whole breast segmentation. The preliminary results on 70 cases show that the proposed method can obtain accurate segmentation of whole breast based on subjective observation. With the manually delineated region of 16 breasts in 8 cases, our method achieved Dice overlap measure of 92.1% ± 1.9% (mean ± SD) and volume agreement of 91.6% ± 4.7% for whole breast segmentation. It took approximately 4 minutes and 2.5 minutes for our method to segment the breast in an MR scan of 160 slices and 108 slices, respectively.
Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis
KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, 3D image reconstruction, Breast cancer, Magnetic resonance imaging, Image segmentation, Computer programming, 3D scanning, 3D image processing
Magnetic resonance (MR) imaging has been widely used for risk assessment and diagnosis of breast cancer in clinic. To
develop a computer-aided diagnosis (CAD) system, breast segmentation is the first important and challenging task. The
accuracy of subsequent quantitative measurement of breast density and abnormalities depends on accurate definition of the breast area in the images. The purpose of this study is to develop and evaluate a fully automated method for accurate segmentation of breast in three-dimensional (3-D) MR images. A fast method was developed to identify bounding box, i.e., the volume of interest (VOI), for breasts. A 3-D spiral scanning method was used to transform the VOI of each breast into a single two-dimensional (2-D) generalized polar-coordinate image. Dynamic programming technique was applied to the transformed 2-D image for delineating the “optimal” contour of the breast. The contour of the breast in the transformed 2-D image was utilized to reconstruct the segmentation results in the 3-D MR images using interpolation and lookup table. The preliminary results on 17 cases show that the proposed method can obtain accurate segmentation of the breast based on subjective observation. By comparing with the manually delineated region of 16 breasts in 8 cases, an overlap index of 87.6% ± 3.8% (mean ± SD), and a volume agreement of 93.4% ± 4.5% (mean ± SD) were achieved, respectively. It took approximately 3 minutes for our method to segment the breast in an MR scan of 256 slices.