Jian Zheng

A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration

Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency.

A Novel Software Platform for Medical Image Processing and Analyzing

The design of software platform for medical imaging application has been increasingly prioritized as the sophisticated application of medical imaging. With this demand, we have designed and implemented a novel software platform in traditional object-oriented fashion with some common design patterns. This platform integrates the mainstream algorithms for medical image processing and analyzing within a consistent framework, including reconstruction, segmentation, registration, visualization, etc., and provides a powerful tool for both scientists and engineers. The overall framework and certain key technologies are introduced in detail. Presented experiment examples, numerous downloads, extensive uses, and practical applications commendably demonstrate the validity and flexibility of the platform.

Retinal fundus image registration via vascular structure graph matching

Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients.

Salient Feature Region: A New Method for Retinal Image Registration

Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration.

Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction

BackgroundMetal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. Therefore, it is essential to reduce these artifacts to meet the clinical demands.MethodsIn this work, we propose a Gaussian diffusion sinogram inpainting metal artifact reduction algorithm based on prior images to reduce these artifacts for fan-beam computed tomography reconstruction. In this algorithm, prior information that originated from a tissue-classified prior image is used for the inpainting of metal-corrupted projections, and it is incorporated into a Gaussian diffusion function. The prior knowledge is particularly designed to locate the diffusion position and improve the sparsity of the subtraction sinogram, which is obtained by subtracting the prior sinogram of the metal regions from the original sinogram. The sinogram inpainting algorithm is implemented through an approach of diffusing prior energy and is then solved by gradient descent. The performance of the proposed metal artifact reduction algorithm is compared with two conventional metal artifact reduction algorithms, namely the interpolation metal artifact reduction algorithm and normalized metal artifact reduction algorithm. The experimental datasets used included both simulated and clinical datasets.ResultsBy evaluating the results subjectively, the proposed metal artifact reduction algorithm causes fewer secondary artifacts than the two conventional metal artifact reduction algorithms, which lead to severe secondary artifacts resulting from impertinent interpolation and normalization. Additionally, the objective evaluation shows the proposed approach has the smallest normalized mean absolute deviation and the highest signal-to-noise ratio, indicating that the proposed method has produced the image with the best quality.ConclusionsNo matter for the simulated datasets or the clinical datasets, the proposed algorithm has reduced the metal artifacts apparently.

Robust Non-Rigid Point Set Registration Using Student's-t Mixture Model

The Students-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Students-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Students-t mixture model centroids. Then, we fit the Students-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD) algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms.

Retinal image registration based on salient feature regions

Retinal image registration is essential and crucial for ophthalmologists to diagnose various diseases. A great number of methods have been developed to solve this problem, however, fast and accurate retinal image registration is still a challenging problem since the great content complexity and low image quality of the unhealthy retina. This paper provides a new retinal image registration method based on salient feature regions (SFR). We first extract the SFR in each image based on a well defined region saliency metric. Next, SFR are matched by using an innovative local feature descriptor. Then we register those matched SFR using local rigid transformation. Finally, we register the two images adopting global second order polynomial transformation with locally rigid registered region centers as control points. Experimental results prove that our method is very fast and accurate, especially quite effective for the low quality retinal images registration.

Low‑dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least‑squares

BackgroundIn order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction.MethodsIn this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares).ResultsThrough the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise.ConclusionThe proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

Retinal image graph-cut segmentation algorithm using multiscale Hessian-enhancement-based nonlocal mean filter.

We propose a new method to enhance and extract the retinal vessels. First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel. By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time. After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel structures. Finally, an accurate graph-cut segmentation step is performed using the result of previous symmetry transformation as an initial. We test the proposed approach on the publicly available databases: DRIVE. The experimental results show that our method is quite effective.

A prior-based metal artifact reduction algorithm for x-ray CT

In computed tomography (CT), metal objects in the scanning filed are accompanied by physical phenomenon that causes projections to be inconsistent. These inconsistencies produce bright and dark shadows or streaks in analytically reconstructed images. Interpolation-based metal artifact reduction (MAR) algorithms usually replace the inconsistent projection data by estimating surrogate data based on the surrounding uncorrupted projections. In such cases, secondary artifacts will be generated when the data estimates are inaccurate. Therefore, better projection estimation is critical. This paper proposes an image post-processing strategy to create an intermediate image, named the prior image and better estimates of the surrogate data by forward projecting this prior image. The proposed method consists of three steps based on the forward projection MAR framework. First, metallic implants in the uncorrected images are segmented using a Markov random field model (MRF). Then a prior image is generated via an edge-preserving filter and a recovery procedure of the adjacent anatomical structures. Finally, the projection is completed via forward projecting this prior image and the corrected image is reconstructed by the filtered backprojection (FBP) method. Studies on both phantom and clinical data are carried out to verify the performance of the proposed method. The comparisons with other previous MAR algorithms demonstrate that the proposed MAR method performs better in metal artifact suppression and anatomical structure preservation.