Shoujun Zhou

Multiresolution Elastic Registration of X-Ray Angiography Images Using Thin-Plate Spline

X-ray angiography, a powerful technique for the visualization of blood vessels, has been widely used in clinical practice. However, due to unavoidable motion of patient, the subtraction images often suffer from misregistration artifacts. In order to improve the quality of subtraction images, registration algorithms are often employed before direct subtraction of mask and live images. A novel multiresolution elastic registration algorithm is proposed for the registration of the digital angiographic images using thin-plate spline (TPS). Our main contribution is a multiresolution search strategy specifically designed for the template matching method. In this strategy, the mask image is decomposed to coarse and fine sub-image blocks iteratively using the pyramid approach. Experimental results show that the multiresolution refinement strategy is well adapted to the template matching method, and can achieve better performance than comparable single step algorithms, because local minima can be overcome by the gradual coarse-to-fine approach that also ensures convergence. Registration results of four typical similarity measures, namely energy of histogram of differences (EHD), mutual information (MI), correlation and sum of squared differences (SSD), are compared. Three different interpolation methods, including nearest-neighbor, bilinear and bicubic, are also tested and compared. The overall conclusion is that the multiresolution refinement algorithm based on EHD combined with the bicubic interpolation method is very robust and effective for the registration of X-ray angiography images, which can obtain sub-pixel registration accuracy and is fully automatic. In addition, the objective measurement method developed in this paper on simulated data makes it possible to quantitatively evaluate the quality of the elastic registration results

A Novel Method of Vessel Segmentation for X-ray Coronary Angiography Images

This paper presents a new automatic region-growing method for vessel segmentation in two-dimensional X-ray coronary angiography images. The method consists of two parts: the feature map extraction based on a novel vesselness function; and the segmentation process which includes automatic seed-point selection, main branch segmentation and vessel detail repair. Both the greyscale and spatial information are extracted for segmentation based on region growing algorithm. The presented method is validated on several clinical X-ray coronary angiography images, and the experimental results show that the method can not only segment large vessels but also small vessels.

Brain MR image denoising for Rician noise using pre-smooth non-local means filter

BackgroundMagnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise.MethodsConsidering the characteristics of both Rician noise and the NLM filter, this study proposes a frame for a pre-smoothing NLM (PSNLM) filter combined with image transformation. In the PSNLM frame, noisy MRI is first transformed into an image in which noise can be treated as additive noise. Second, the transformed MRI is pre-smoothed via a traditional denoising method. Third, the NLM filter is applied to the transformed MRI, with weights that are computed from the pre-smoothed image. Finally, inverse transformation is performed on the denoised MRI to obtain the denoising results.ResultsTo test the performance of the proposed method, both simulated and real patient data are used, and various pre-smoothing (Gaussian, median, and anisotropic filters) and image transformation [squared magnitude of the MRI, and forward and inverse variance-stabilizing trans-formations (VST)] methods are used to reduce noise. The performance of the proposed method is evaluated through visual inspection and quantitative comparison of the peak signal-to-noise ratio of the simulated data. The real data include Alzheimer’s disease patients and normal controls. For the real patient data, the performance of the proposed method is evaluated by detecting atrophy regions in the hippocampus and the parahippocampal gyrus.ConclusionsThe comparison of the experimental results demonstrates that using a Gaussian pre-smoothing filter and VST produce the best results for the peak signal-to-noise ratio (PSNR) and atrophy detection.

Automatic Segmentation of Coronary Angiograms Based on Probabilistic Tracking

This paper presents a novel tracking method for automatic segmentation of coronary artery tree in the X-ray angiographic images, based on probabilitistic vessel tracking and structure pattern inferring. The method is composed of two main steps, namely preprocessing, and tracking. In the preprocessing step, multiscale Gabor filtering and Hessian matrix analysis are used to enhance and extract vessels from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In the tracking step, a probabilistic tracking operator is proposed to extract vessel segments or branches, together with a detector to identify vessel structure. The identified structure pattern is used to control the tracking process. By appropriate integration of these advanced preprocessing and tracking steps, the algorithm is able to extract both vessel axis-lines and edge points, and to measure the arterial diameters in various complicated cases. The experimental results were satisfying.

Fast automatic 3D liver segmentation based on a three‐level AdaBoost‐guided active shape model

PURPOSE A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. METHODS The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. RESULTS The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. CONCLUSIONS The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

3D Model-based method for vessel segmentation in TOF-MRA

In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF)-MRA data. The segmentation results give a good performance in extracting blood vessels.

Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

A new method for robust contour tracking in cardiac image sequences

For the segmentation and robust tracking of the cardiac image sequences (CIS) of magnetic resonance (MR), an optimized algorithm is presented in this paper, which is based on the active contour framework. To use the active contours model (ACM) estimating the cardiac motion, a new concept of generalized fuzzy gradient vector flow (GFGVF) is presented and compared with the classical gradient vector flow (GVF). Then a modified ACM is proposed for motion tracking, which is based on two new external forces: one is the GFGVF field; the other is the relativity of optical flow field (OFF) on predictive contour. For robust tracking the outline of interest, a set of motion equations is presented to describe two correlative updating steps. Another, given some prior terms and likelihood one, the motion state of each point can be found by the maximum a posteriori probability (MAP).

Optical and thermal properties of a cyanine dye medium for next-generation DVD-Rs

AbstractA dye material for the next generation of digital versatile disk-recordables (high-definition DVD-Rs) is required to absorb at a shorter wavelength compared with conventional dye media. For this purpose, l,3,3,l’,3’,3’-hexamethyl-2,2’-indocyanine perchlorate (D-l), whose maximum absorption band exists at 434.5 nm, was selected. Reflection and transmission spectra of D-l thin films were studied. Oscillation of the reflectance and transmittance around 480 nm with film thickness can be seen. The calculated complex refractive index is 2.15 + i0.085. Its decomposition temperature was measured to be around 282°C and no melting point was observed in its differential scanning calorimeter (DSC) curve. The optical and thermal properties of D-l and longer wavelength-absorbing indocyanine dyes were also compared.

Inferring vascular structures in coronary artery x-ray angiograms based on multi-feature fuzzy recognition algorithm

The multi-feature fuzzy recognition (MFFR) algorithm was presented to infer the vessel structures, in the context of X-Ray Angiograms (XRA) of the coronary artery. In the modeling, a multi-feature metrics (MFM) was firstly established to describe the local configuration; then the membership degree of MFM-based fuzzy subsets was defined, and the fuzzy recognition operator was constructed. The MFFR algorithm can correctly infer four kinds of vessel structures including vascular ends, segments, bifurcations and crossovers. The results are satisfying: on average 91.1% of the testing vessel lengths in medium quality images are automatically delineated as well as their structures being correctly inferred with point-wise.