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Bone is one of the most important anatomical structures in humans and osteoporosis is one of the major public health concerns in the world. Osteoporosis is a main target disease of bone, which can be detected by medical image techniques. The purpose of this study is to develop a fully automated computer scheme to measure bone-mineral-density (BMD) values for vertebral trabecular bones. This scheme...
The purpose of this study is to recognize the psoas major muscle on X-ray CT images. For this purpose, we propose a novel recognition method. The recognition process in this method involves three steps: the generation of a shape model for the psoas major muscle, recognition of anatomical points such as the origin and insertion, and the recognition of the psoas major muscles by the use of the shape...
The spleen is always hypertrophied in the patient with cirrhotic liver, which is regarded as one of complementary features for analysis of cirrhosis. Furthermore, segmentation of an abnormal spleen region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the spleen to other organs or the shape,...
This communication describes a fully automated method by which the position of the diaphragm surface can be estimated by deforming a thin-plate model to match the bottom surface of the lung in CT images. This method was applied to 338 X-ray CT scans, and its validity was proved by the experimental results.
The anatomical human structure recognition is very important and necessary during the development of computer-aided diagnosis (CAD) system. In this paper, we propose an image processing scheme that can recognize the general structure of human torso by identifying the human torso region from CT images automatically and separating it into 7 parts: skin, subcutaneous fat, muscle, bone, diaphragm, thoracic...
We have developed a new recognition approach using 2nd order autocorrelation and multi-regression analysis to detect a small (<7mm in diameter) lung nodules in chest 3D CT images. By combining our previous detection method of the template matching based on genetic algorithm, the detection performance was 94% true-positive rate at 2.05 false-positive marks per case using leave-one-out study
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