Shu Huazhong

Blood vessels segmentation in retina via wavelet transforms using steerable filters

This paper presents an efficient method for automatic segmentation of blood vessels in retinal images. A set of directional basis filters based on dyadic wavelet transform is designed to enhance blood vessels. It attempts to utilize the linear combination of the wavelet transforms to fix on the blood vessels directional information in retinal images. The directional maps are processed by thresholding scheme in order to segment blood vessels from the background. The proposed thresholding approach evaluates 2-D entropies based on the gray level-gradient co-occurrence matrix. The 2-D thresholding vector that maximizes the edge class entropies is selected. The thresholding method utilizes the gray level and gradient information in the enhanced image. The new method promises the simpleness and flexibility in many image enhancement and segmentation applications.

Detection of Cerebral Vessels in MRA Using 3D Steerable Filters

This paper describes a fully automatic method for enhancement and segmentation of three-dimensional(3D) cerebral vessels in MRA. We obtain the 3D dyadic B-spline wavelets by extending corresponding 1D wavelet. A 3D steerable filter is then developed based on 3D dyadic B-spline wavelets. One can adaptively steer the filter to an arbitrary direction. The oriented energy of filter response is introduced for detecting orientation strength of vessels in that direction. The points with maximum of local oriented energy across multiple scales are regarded as vessel points. This method was tested on real MRA data and promising results have been obtained. It could be suitable for other types of curvilinear structures such as cardiovascular vessels, bronchial tree

A row-action alterative to the space-alternating generalized expectation-maximization algorithm for image reconstruction in positron emission tomography

The space-alternating generalized expectation (SAGE) maximization algorithm has been successfully used in image reconstruction due to its rapid convergence. In this paper, a row-action alternative to the SAGE algorithm (RASAGE) is proposed; it processes the projection data sequentially. In order to speed up the convergence rate, we process the projection lines using a special order in such a way that the sequential projection lines are independent of each other. A relaxation parameter is also used to adjust the projection data update level. Comparison of the RASAGE with SAGE algorithm shows that the former method converges faster than the latter.