Diagnostic imaging of the respiratory disease is feasible to understand by pattern recognition. Abnormal opacities of lung are divided into consolidation, small linear and nodular opacities, mass, atelectases, and linear opacities. Some diseases are shown normal which has subtle abnormalities in chest CT. Majority of diagnostic radiologist now realize that the CAD derived efficient and accurate information for them to perform image diagnoses. It is inevitable both radiologist and CAD researchers should collaborate together to develop more efficient CAD programs
We analysed of the images generated with multi-slice CT in the longitudinal direction (Z) and assessed the noise equivalent quanta (NEQ)evaluation. Evaluation of image quality in NEQ, was possible to accuracy of reappearance in low at high frequency characteristics. In addition, NEQ information was confirmed of usefulness in compared with Winer spectra and MTF method. Examination results suggest that the quantitative analysis in characteristics of image noise and image resolution at multi-slice CT images can provide an optimal parameter for improving quality of images in clinical data.
An automatic detection scheme for the nipple region on digital mammograms was developed. An automatic curve analysis based on angle estimation between two vectors was mainly employed. After breast border following, the curve degree at every pixel along the breast border was compared. Finally a skin line segment in the interest regions was determined as the nipple outline. A total of 215 digital mammograms, including left and right breasts, were processed using this method. The results showed that the nipples were correctly detected in 95% of all cases.