Qinmu Peng

Segmentation of retinal blood vessels using the radial projection and semi-supervised approach

Automatic segmentation of retinal blood vessels has become a necessary diagnostic procedure in ophthalmology. The blood vessels consist of two types of vessels, i.e., thin vessels and wide vessels. Therefore, a segmentation method may require two different processes to treat different vessels. However, traditional segmentation algorithms hardly draw a distinction between thin and wide vessels, but deal with them together. The major problems of these methods are as follows: (1) If more emphasis is placed on the extraction of thin vessels, the wide vessels tend to be over detected; and more artificial vessels are generated, too. (2) If more attention is paid on the wide vessels, the thin and low contrast vessels are likely to be missing. To overcome these problems, a novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method is presented in this paper. The radial projection method is used to locate the vessel centerlines which include the low-contrast and narrow vessels. Further, we modify the steerable complex wavelet to provide better capability of enhancing vessels under different scales, and construct the vector feature to represent the vessel pixel by line strength. Then, semi-supervised self-training is used for extraction of the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels. Our approach is tested on two publicly available databases. Experiment results show that the method can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions.

Eye Gaze Tracking With a Web Camera in a Desktop Environment

This paper addresses the eye gaze tracking problem using a low cost and more convenient web camera in a desktop environment, as opposed to gaze tracking techniques requiring specific hardware, e.g, infrared high-resolution camera and infrared light sources, as well as a cumbersome calibration process. In the proposed method, we first track the human face in a real-time video sequence to extract the eye regions. Then, we combine intensity energy and edge strength to obtain the iris center and utilize the piecewise eye corner detector to detect the eye corner. We adopt a sinusoidal head model to simulate the 3-D head shape, and propose an adaptive weighted facial features embedded in the pose from the orthography and scaling with iterations algorithm, whereby the head pose can be estimated. Finally, the eye gaze tracking is accomplished by integration of the eye vector and the head movement information. Experiments are performed to estimate the eye movement and head pose on the BioID dataset and pose dataset, respectively. In addition, experiments for gaze tracking are performed in real-time video sequences under a desktop environment. The proposed method is not sensitive to the light conditions. Experimental results show that our method achieves an average accuracy of around 1.28° without head movement and 2.27° with minor movement of the head.

A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance

This paper presents a visual saliency detection approach, which is a hybrid of local feature-based saliency and global feature-based saliency (simply called local saliency and global saliency, respectively, for short). First, we propose an automatic selection of smoothing parameter scheme to make the foreground and background of an input image more homogeneous. Then, we partition the smoothed image into a set of regions and compute the local saliency by measuring the color and texture dissimilarity in the smoothed regions and the original regions, respectively. Furthermore, we utilize the global color distribution model embedded with color coherence, together with the multiple edge saliency, to yield the global saliency. Finally, we combine the local and global saliencies, and utilize the composition information to obtain the final saliency. Experimental results show the efficacy of the proposed method, featuring: 1) the enhanced accuracy of detecting visual salient region and appearance in comparison with the existing counterparts, 2) the robustness against the noise and the low-resolution problem of images, and 3) its applicability to multisaliency detection task.

Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification

The low-contrast and narrow blood vessels in retinal images are difficult to be extracted but useful in revealing certain systemic disease. Motivated by the goals of improving detection of such vessels, we propose the radial projection method to locate the vessel centerlines. Then the supervised classification is used for extracting the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels after removal schemes. Our approach is tested on the STARE database, the results demonstrate that our algorithm can yield better segmentation.

A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number

It is usually hard to predetermine the true number of segments in lip segmentation. This paper, therefore, presents a clustering-based approach to lip segmentation without knowing the true segment number. The objective function in the proposed approach is a variant of the partition entropy (PE) and features that the coincident cluster centroids in pattern space can be equivalently substituted by one centroid with the function value unchanged. It is shown that the minimum of the proposed objective function can be reached provided that: 1) the number of positions occupied by cluster centroids in pattern space is equal to the true number of clusters and 2) these positions are coincident with the optimal cluster centroids obtained under PE criterion. In implementation, we first randomly initialize the clusters provided that the number of clusters is greater than or equal to the ground truth. Then, an iterative algorithm is utilized to minimize the proposed objective function. For each iterative step, not only is the winner, i.e., the centroid with the maximum membership degree, updated to adapt to the corresponding input data, but also the other centroids are adjusted with a specific cooperation strength, so that they are each close to the winner. Subsequently, the initial overpartition will be gradually faded out with the redundant centroids superposed over the convergence of the algorithm. Based upon the proposed algorithm, we present a lip segmentation scheme. Empirical studies have shown its efficacy in comparison with the existing methods.

Automatic mitral valve leaflet tracking in Echocardiography via constrained outlier pursuit and region-scalable active contours

Tracking the mitral valve leaflet in Echocardiography is of crucial importance to the better understanding of various cardiac diseases and is very helpful to assist the surgical intervention for mitral valve repair. In this paper, we present an automatic mitral leaflet motion tracking approach, which consists of two phases: constrained outlier pursuit for mitral leaflet detection and its shape refinement. In the former phase, we first learn a low-rank subspace which can gradually change over time to model the background sequence, and simultaneously detect sparse outliers through such low-rank representation. Then, we extract the supported states of the myocardial tissues to constrain the outlier pursuit for mitral leaflet detection, featuring on reliably removing the irrelevant outliers. In the latter phase, we further present a region-scalable active contour to refine the shapes of the detected mitral leaflet for final tracking. The proposed approach does not require any user-specified interactive information or pre-collected training data for learning. The robustness of its performance has been demonstrated against the fast mitral leaflet motions, shape deformation and unstable myocardial tissue appearance. Experimental results have shown that the proposed approach performs favorably on four challenging sequences in comparison with the state-of-the-art methods.

FINGERPRINT ENHANCEMENT BASED ON WAVELET AND ANISOTROPIC FILTERING

The importance of high-fidelity enhancement in low quality fingerprint image cannot be overemphasized. Most of the existing fingerprint enhancement methods are contextual filter-based methods and they often suffer from two shortcomings: (1) there is block effect on the enhanced images; and (2) they blur or destroy ridge structures around singular points. In order to well preserve the ridge structures in singular regions and avoid block effect, we develop a new method for fingerprint enhancement combining nontensor product wavelet filter banks and anisotropic filter. We first decompose the fingerprint image using the nontensor product wavelet filter banks. Then we modify the approximation subimage using anisotropic filtering and adjust the high frequency coefficients of the three other subimages by applying the adaptive approach to reduce the noises according to the geometry feature of images. Finally, the inverse transform is applied to map the result and a final contrast enhancement is done subsequently. Experiments have been conducted on the fingerprint database FVC2004 in our study. The results demonstrate that the proposed approach is capable of overcoming block effect and enhancing low quality fingerprint while preserving the ridge structures around singular points.

Sample Outlier Detection Based on Local Kernel Regression

Outlier often degrades the classification and cluster accuracy. In this paper, we present an outlier detection approach based on local kernel regression for instance selection. It evaluates the reconstruction error of instances by their neighbors to identify the outliers. Experiments are performed both on the synthetic and real-life data sets to show the efficacy of the proposed approach in comparison with the existing counterparts.

A joint space-angle regularization approach for single 4D diffusion image super-resolution

Low signal‐to‐noise‐ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space‐angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down‐sampled in both 3D‐space and q‐space.