The purpose of this study is to develop a technique for computer-aided diagnosis (CAD) systems to detect lung nodules in helical X-ray pulmonary computed tomography CT) images. We propose a novel template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules existing within the lung area; the GA was used to determine the target position in the observed image efficiently and to select an adequate template image from several reference patterns for quick template matching. In addition, a conventional template matching was employed to detect nodules existing on the lung wall area, lung wall template matching (LWTM), where semicircular models were used as reference patterns; the semicircular models were rotated according to the angle of the target point on the contour of the lung wall. After initial detecting candidates using the two template-matching methods, we extracted a total of 13 feature values and used them to eliminate false-positive findings. Twenty clinical cases involving a total of 557 sectional images were used in this study. 71 nodules out of 98 were correctly detected by our scheme (i.e., a detection rate of about 72%), with the number of false positives at approximately 1.1/sectional image. Our present results show that our scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.
Preoperative redistribution by TAE reduced the drop in gastric TBF during preparation of a gastric tube and helped prevent postoperative anastomotic leakage in esophageal reconstruction.
A simple method to determine the state of ischaemia or fibrosis of myocardial cells has been developed. This method uses the ST wave of 64-channel magnetocardiogram (MCG) signals to calculate three parameters from the current-arrow map of the normal component signal of the MCG. One parameter is a total current vector that is obtained through summation of all current arrows. Another is a variance current vector calculated from the differential vector of two total current vectors at different times. The third is a flatness factor between the magnitude of the total current vector and the variance current vector. The three parameters are independent of the distance between the heart and the gradiometers. We measured the MCG signals of 29 healthy subjects, twenty patients with coronary artery disease (ten with previous myocardial infarction (MI) and ten with angina pectoris (AP)), and eight patients with cardiomyopathy (four with hypertrophic cardiomyopathy (HCM), three with dilated cardiomyopathy (DCM), and one with restrictive cardiomyopathy (RCM)). With our method, none of the healthy subjects tested positive for myocardial abnormalities, while 80% of the MI patients, 50% of the AP patients, and 100% of the cardiomyopathy patients tested positive. Although further testing is needed, we feel this simple technique enables easy diagnosis of myocardial damage.
Twenty patients with mucin-producing pancreatic tumor and 60 with other pancreatic diseases underwent computed tomography (CT) to establish the CT characteristics of mucin-producing pancreatic tumor. Scans were obtained with thin sections by administering a large volume of contrast material (200 mL). Mucin-producing pancreatic tumors were divided into three subgroups, and the CT characteristics were as follows: Main duct type tumors consisted of a cystic mass in or communicating with the dilated main pancreatic duct (MPD). Excrescent nodules and/or septa were found in the cyst. The MPD was markedly dilated over its entire length. Branch duct type tumors consisted of clustered small cysts that were all approximately the same size in diameter (1-2 cm). Excrescent nodules or septa were not always seen. The MPD near the lesion was often slightly dilated. Peripheral type tumors consisted of a well-defined cystic mass with excrescent nodules and/or septa. Even if the cyst was multilocular, a large main cyst was in it. The MPD usually was not dilated. The CT findings corresponded to macroscopic findings. Mucin-producing pancreatic tumor can be differentiated from other pancreatic diseases with these criteria.
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