• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.387-413
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• 1998

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(P<0.05). 3. There were significant differences of SNR values between 60kVp and 70kVp in seven interpolation methods. There were significant differences of SNR values between facet model method and those of the other methods at 60kVp(P<0.05), but there were not significant differences of SNR values among seven interpolation methods at 70kVp(P>0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods (P< 0.05). 5. The speed of computation time was the fastest with nearest -neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.273-281
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• 2010

In this paper, we propose an automatic segmentation method of the endo- and epicardial boundary by using ray-casting profile based on intensity distribution and gradient information in CT images. First, endo-cardial boundary points are detected by using adaptive thresholding and seeded region growing. To include papillary muscles inside the boundary, the endo-cardial boundary points are refined by using ray-casting based profile. Second, epi-cardial boundary points which have both a myocardial intensity value and a maximum gradient are detected by using ray-casting based adaptive gradient profile. Finally, to preserve an elliptical or circular shape, the endo- and epi-cardial boundary points are refined by using elliptical interpolation and B-spline curve fitting. Then, curvature-based contour fitting is performed to overcome problems associated with heterogeneity of the myocardium intensity and lack of clear delineation between myocardium and adjacent anatomic structures. To evaluate our method, we performed visual inspection, accuracy and processing time. For accuracy evaluation, average distance difference and overalpping region ratio between automatic segmentation and manual segmentation are calculated. Experimental results show that the average distnace difference was $0.56{\pm}0.24mm$. The overlapping region ratio was $82{\pm}4.2%$ on average. In all experimental datasets, the whole process of our method was finished within 1 second.