Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Automated IVUS Contour Detection Using Intesity Features and Radial Basis Function Approximation
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Analysis of multichannel narrow-band filters for image texturesegmentation
IEEE Transactions on Signal Processing
An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames
IEEE Transactions on Information Technology in Biomedicine
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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The detection of the lumen contour in the Intravascular Ultrasound (IVUS) image plays a very important part in assessing atherosclerosis. However, for the images at a high sampling frequency, the blood signals make it difficult to detect the lumen contours. In this paper, a new segmentation method is proposed and implemented that detects the lumen contour in IVUS images automatically. The method is based on the difference of the texture features between the blood signals and the vessel wall. During preprocessing of the raw IVUS images, the method successfully removed the artificial noise, which was caused by the sampling catheter. After that, the lumen texture features and the vessel wall texture features were able to be distinguished through applying the Gabor wavelet transformation. Based on the distinguished texture features, the lumen contour was detected and refined smoothly. The experiment results indicate that the lumen contour in the raw IVUS image can be detected completely automatically and accurately.