Yitian Zhao

Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L2 Lebesgue measure of the γ-neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a features boundaries (i.e., H1 Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct features segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observers annotations.

Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy.

The detection and assessment of intravascular filling defects is important, because they may represent a process central to cerebral malaria pathogenesis: neurovascular sequestration. We have developed and validated a framework that can automatically detect intravascular filling defects in fluorescein angiogram images. It first employs a state-of-the-art segmentation approach to extract the vessels from images and then divide them into individual segments by geometrical analysis. A feature vector based on the intensity and shape of saliency maps is generated to represent the level of abnormality of each vessel segment. An AdaBoost classifier with weighted cost coefficient is trained to classify the vessel segments into normal and abnormal categories. To demonstrate its effectiveness, we apply this framework to 6,358 vessel segments in images from 10 patients with malarial retinopathy. The test sensitivity, specificity, accuracy, and area under curve (AUC) are 74.7%, 73.5%, 74.1% and 74.2% respectively when compared to the reference standard of human expert manual annotations. This performance is comparable to the agreement that we find between human observers of intravascular filling defects. Our method will be a powerful new tool for studying malarial retinopathy.

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

Saliency driven vasculature segmentation with infinite perimeter active contour model

Abstract Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors.

Conditional random field-based mesh saliency

We propose a new method for detecting mesh saliency, a reflection of perception-based regional importance for 3D meshes. The basic idea is to incorporate the Conditional Random Field (CRF) framework with a saliency detection process. We first produce a multi-scale representation for a mesh. Then, a CRF is designed to robustly detect salient regions utilising neighbourhood consistency. By inferring the CRF via belief propagation algorithm, we actually make use of the global statistic information in the saliency detection process. Experimental results demonstrate the robustness and the effectiveness of the proposed method.

Convex hull indexed Gaussian mixture model (CH-GMM) for 3D point set registration

To solve the problem of rigid/non-rigid 3D point set registration, a novel convex hull indexed Gaussian mixture model (CH-GMM) is proposed in this paper. The model works by computing a weighted Gaussian mixture model (GMM) response over the convex hull of each point set. Three conditions, proximity, area conservation and projection consistency, are incorporated into the model so as to improve its performance. Given that the convex hull is the tightest convex set of a point set, the combination of Gaussian mixture and convex hull can effectively preserve the topological structure of a point set. Furthermore, computational complexity can be significantly reduced since only the GMM of the convex hull (instead of the whole point set) needs to be calculated. Rigid registration is achieved by seeking the best rigid transformation parameters yielding the most similar CH-GMM responses. Non-rigid deformation is realized by optimizing the coordinates of the control points used by the thin-plate spline model for interpolating the entire point set. Experiments are designed to evaluate a methods robustness to rotational changes between two point sets, positional noise, differences in density and partial overlap. The results demonstrated better robustness and registration accuracy of CH-GMM based method over state-of-the-art methods including iterative closest point, coherent point drift and the GMM method. Besides, the computation of CH-GMM is efficient. A novel CH-GMM method is proposed for non-rigid registration of point sets.The CH-GMM can effectively present the topological information of point set.The CH-GMM matching of the point sets is very fast.The registration is very robust for points with large differences.

Automated Detection of Leakage in Fluorescein Angiography Images with Application to Malarial Retinopathy

The detection and assessment of leakage in retinal fluorescein angiogram images is important for the management of a wide range of retinal diseases. We have developed a framework that can automatically detect three types of leakage (large focal, punctate focal, and vessel segment leakage) and validated it on images from patients with malarial retinopathy. This framework comprises three steps: vessel segmentation, saliency feature generation and leakage detection. We tested the effectiveness of this framework by applying it to images from 20 patients with large focal leak, 10 patients with punctate focal leak, and 5,846 vessel segments from 10 patients with vessel leakage. The sensitivity in detecting large focal, punctate focal and vessel segment leakage are 95%, 82% and 81%, respectively, when compared to manual annotation by expert human observers. Our framework has the potential to become a powerful new tool for studying malarial retinopathy, and other conditions involving retinal leakage.

3-Points Convex Hull Matching (3PCHM) for fast and robust point set registration

Point set registration plays a crucial role in numerous computer vision applications. This paper proposes a novel and general approach called three-point convex hull matching (3PCHM) for registering two point sets with similarity transform. First, convex hulls are extracted from both point sets. Triangular patches on the surface of convex hulls are specified by predefining their normal vectors, thus guaranteeing that all points are located on the same side of any randomly selected triangle plane. Second, the potential similar triangle pair set is obtained by comparing the length ratio of the edges on the two extracted convex hulls. Thereafter, the transformation parameters for each pairwise triangle are calculated by minimizing the Euclidean distance between the corresponding vertex pairs. Furthermore, a k-dimensional (k-d) tree is used to accelerate the closest point search for the whole point sets. Third, outliers that may lead to significant errors are discarded by integrating the random sample consensus algorithm for global optimization. Experiments show that the proposed 3PCHM is robust even with the existence of noise and outliers and is effective in cases of part-to-part registration and part-to-whole registration.

Augmented reality based real-time subcutaneous vein imaging system.

A novel 3D reconstruction and fast imaging system for subcutaneous veins by augmented reality is presented. The study was performed to reduce the failure rate and time required in intravenous injection by providing augmented vein structures that back-project superimposed veins on the skin surface of the hand. Images of the subcutaneous vein are captured by two industrial cameras with extra reflective near-infrared lights. The veins are then segmented by a multiple-feature clustering method. Vein structures captured by the two cameras are matched and reconstructed based on the epipolar constraint and homographic property. The skin surface is reconstructed by active structured light with spatial encoding values and fusion displayed with the reconstructed vein. The vein and skin surface are both reconstructed in the 3D space. Results show that the structures can be precisely back-projected to the back of the hand for further augmented display and visualization. The overall system performance is evaluated in terms of vein segmentation, accuracy of vein matching, feature points distance error, duration times, accuracy of skin reconstruction, and augmented display. All experiments are validated with sets of real vein data. The imaging and augmented system produces good imaging and augmented reality results with high speed.