25 April 1997 Adaptive edge enhancement based on image segmentation
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Abstract
Many clinical applications, e.g., inner auditory cannel (IAC) studies, demand the CT scanner to provide high spatial in-plane resolution. Currently, these studies are performed by reconstructing the images with a high resolution reconstruction kernel. The cutoff frequency of the kernel is set to the limit of the Nyquist frequency, assuming perfect double sampling per detector cell can be achieved. Because of the fan-beam geometry, patient motion, and the inherent limitations of the third generation CT sampling, the Nyquist criteria are not always strictly observed. As a result, many clinical images are degraded by aliasing artifacts. In many cases, the fine structure of the anatomy and important pathologies are marred by aliasing streaks, which render the image unusable. In this paper, we analyze the root cause of the aliasing artifact and present an adaptive edge enhancement algorithm that enhances the fine structures and suppress aliasing artifacts and noise in the IAC images. In the proposed scheme, a high resolution CT image is first reconstructed with a modified reconstruction kernel, H1(f), which has a frequency response and a cutoff frequency just below the point where significant aliasing artifact can be observed. The reconstructed image is then segmented into two classes (E: enhancement and S: suppression) based on CT numbers as well as texture. Adaptive edge enhancement is performed on the E class and adaptive noise suppression is performed on the S class. Various phantom and clinical studies were conducted. For each case, three images were generated: CT images reconstructed with the conventional high resolution kernel, images reconstructed with the modified H1 kernel, and images produced by the adaptive enhancement algorithm. The results were reviewed by the experts. The conclusion has been fairly consistent that the adaptive edge enhanced images are as sharp as the convectional high resolution CT images, with much reduced noise and aliasing artifacts. Since the segmentation relies on CT numbers as well as the texture in the image, the method is quite robust.
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Jiang Hsieh, "Adaptive edge enhancement based on image segmentation", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274125; https://doi.org/10.1117/12.274125
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