Architectural distortion is one of the most important findings when evaluating mammograms for breast cancer. Abnormal breast architecture is characterized by the presence of spicules, which are distorted mammary structures that are not accompanied by an increased density or mass. We have been developing an automated method for detecting spiculated architectural distortions by analyzing linear structures extracted by normal curvature. However, some structures that are possibly related to distorted areas are not extracted using this method. The purpose of this study was to develop a new automated method for direction analysis of linear structures to improve detection performance in mammography. The direction of linear structures in each region of interest (ROI) was first determined using a direction filter and a background filter that can define one of eight directions (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°). The concentration and isotropic indexes were calculated using the determined direction of the linear structures in order to extract the candidate areas. Discriminant analysis was performed to eliminate false positives results. Our database consisted of 168 abnormal images containing 174 distorted areas and 580 normal images. The sensitivity of the new method was 81%. There were 2.6 and 4.2 false positives per image using the new and previous methods, respectively. These findings show that our new method is effective for detecting spiculated architectural distortions.

It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

Objectives: To explore the feasibility of Vector-Field DXR (VF-DXR) using optical flow method (OFM). Methods: Five healthy volunteers and five COPD patients were studied. DXR was performed in the standing position using a prototype X-ray system (Konica Minolta Inc., Tokyo, Japan). During the examination, participants took several tidal breaths and one forced breath. DXR image file was converted to the videos with different frames per second (fps): 15 fps, 7.5 fps, five fps, three fps, and 1.5 fps. Pixel-value gradient was calculated by the serial change of pixel value, which was subsequently converted mathematically to motion vector using OFM. Color-coding map and vector projection into horizontal and vertical components were also tested. Results: Dynamic motion of lung and thorax was clearly visualized using VF-DXR with an optimal frame rate of 5 fps. Color-coding map and vector projection into horizontal and vertical components were also presented. VF-DXR technique was also applied in COPD patients. Conclusion: The feasibility of VF-DXR was demonstrated with small number of healthy subjects and COPD patients. Advances in knowledge: A new Vector-Field Dynamic X-ray (VF-DXR) technique is feasible for dynamic visualization of lung, diaphragms, thoracic cage, and cardiac contour.

Background We assessed the difference in lung motion during inspiration/expiration between chronic obstructive pulmonary disease (COPD) patients and healthy volunteers using vector-field dynamic x-ray (VF-DXR) with optical flow method (OFM). Methods We enrolled 36 COPD patients and 47 healthy volunteers, classified according to pulmonary function into: normal, COPD mild, and COPD severe. Contrast gradient was obtained from sequential dynamic x-ray (DXR) and converted to motion vector using OFM. VF-DXR images were created by projection of the vertical component of lung motion vectors onto DXR images. The maximum magnitude of lung motion vectors in tidal inspiration/expiration, forced inspiration/expiration were selected and defined as lung motion velocity (LMV). Correlations between LMV with demographics and pulmonary function and differences in LMV between COPD patients and healthy volunteers were investigated. Results Negative correlations were confirmed between LMV and % forced expiratory volume in one second (%FEV1) in the tidal inspiration in the right lung (Spearman’s rank correlation coefficient, rs = -0.47, p < 0.001) and the left lung (rs = -0.32, p = 0.033). A positive correlation between LMV and %FEV1 in the tidal expiration was observed only in the right lung (rs = 0.25, p = 0.024). LMVs among normal, COPD mild and COPD severe groups were different in the tidal respiration. COPD mild group showed a significantly larger magnitude of LMV compared with the normal group. Conclusions In the tidal inspiration, the lung parenchyma moved faster in COPD patients compared with healthy volunteers. VF-DXR was feasible for the assessment of lung parenchyma using LMV.

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