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Biomedical subjects

Ya-Hui Shiao

Publications and source records attributed to Ya-Hui Shiao.

2 recordsLinked to original sources
PubMed2006

Polygon interpolation for serial cross sections.

In this paper, a new technique for contour interpolation between slices is presented. We assumed that contour interpolation is equivalent to the interpolation of a polygon that approximates the object shape. The location of each polygon vertex is characterized by a set of parameters. Polygon interpolation can be performed on these parameters. These interpolated parameters are then used to reconstruct the vertices of the new polygon. Finally, the contour is approximated from this polygon using a cubic spline interpolation. This new technique takes into account the shape, the translation, the size, and the orientation of the object's contours. A comparison with regular shape-based interpolation is made on several object contours. The preliminary results show that this new method yields a better contour and is computationally more efficient than shape-based interpolation. This technique can be applied to gray-level images too. The interpolation result of an MR image does not show artifact of intermediate substance commonly seen in a typical linear gray-level interpolation.

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PubMed2006

A blurring index for medical images.

This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Moran's Z histogram and the original image. The peak ratio of Moran's Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.

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