Luo Li-min

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.

Scatter correction for cone-beam computed tomography using self-adaptive scatter kernel superposition

The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.

Robust ordering of independent spatial components of fMRI data using canonical correlation analysis

The lack of consistent ordering of components resulted from independent component analysis poses a significant obstacle to the pervasive application of this method on fMRI data analysis. Based on the temporal correlation of physiological noise components of fMRI data and that of cerebrospinal fluid data, the ordering of independent spatial components is ranked using canonical correlation analysis. The proposed method can robustly identify the task-related spatial component without any prior information about the functional activation paradigm. The experimental results of analyzing the real fMRI data show the reliability of the presented method.

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.