Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet ...Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet transform is efficient as an edge detector, a l ot of false edges will be extracted from a SAR image, especially in areas of hig h reflectivity, with wavelet transform and a fixed threshold because SAR images are always contaminated by speckle. Therefore, this paper develops an algorithm by using a variable threshold with constant false alarm rates to help suppressin g the speckle in SAR images. Finally, two simulating tests are given and the res ults show that the effect of the multiplicative speckle is controlled and there are few false edges in the gotten edge images. Moreover, owing to the threshold from the wavelet transform coefficients, the algorithm can assure the real time of SAR image aided navigation.展开更多
Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and aco...Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.展开更多
文摘Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet transform is efficient as an edge detector, a l ot of false edges will be extracted from a SAR image, especially in areas of hig h reflectivity, with wavelet transform and a fixed threshold because SAR images are always contaminated by speckle. Therefore, this paper develops an algorithm by using a variable threshold with constant false alarm rates to help suppressin g the speckle in SAR images. Finally, two simulating tests are given and the res ults show that the effect of the multiplicative speckle is controlled and there are few false edges in the gotten edge images. Moreover, owing to the threshold from the wavelet transform coefficients, the algorithm can assure the real time of SAR image aided navigation.
文摘Intravascular ultrasound (IVUS) is a new technology for the diagnosis of coronary artery disease, and for the support of coronary intervention. IVUS image segmentation often encounters difficulties when plaque and acoustic shadow are present A novel approach for hard plaque recognition and media-adventitia border detection of IVUS images is presented in this paper. The IVUS images were first enhanced by a spatial-frequency domain filter that was constructed by the directional filter and histogram equalization. Then, the hard plaque was recognized based on the intensity variation within different regions that were obtained using the k-means algorithm. In the next step, a cost matrix representing the probability of the media-adventitia border was generated by combining image gradient, plaque location and image intensity. A heuristic graph-searching was applied to find the media-adventitia border from the cost matrix.Experiment results showed that the accuracy of hard plaque recognition and media-adventitia border detection was 89.94% and 95.57%, respectively. In conclusion,using hard plaques recognition could improve media-adventitia border detection in IVUS images.
