Interpolation of 3-D binary images based on morphological skeletonization

In this paper, the morphological skeleton interpolation (MSI) algorithm is presented. It is an efficient, shape-based interpolation method used for interpolating slices in a three-dimensional (3-D) binary object. It is based on morphological skeletonization, which is used for two-dimensional (2-D) slice representation. The proposed morphological skeleton matching process provides translation, rotation, and scaling information at the same time. The interpolated slices preserve the shape of the original object slices, when the slices have similar shapes. It can also modify the shape of an object when the successive slices do not have similar shapes. Applications on artificial and real data are also presented.     

Shape-based interpolation of tree-like structures in three-dimensional images

Many three-dimensional (3-D) medical images have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the missing slices. Unfortunately, when objects are extracted and displayed from the interpolated image, they often exhibit a blocky and generally unsatisfactory appearance, a problem that is particularly acute for thin treelike structures such as the coronary arteries. Two methods for shape-based interpolation that offer an improvement to linear interpolation are presented. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image, and the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds on the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.     

Registration-based interpolation

A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the registration algorithm. The registration-based method is compared to both standard linear interpolation and shape-based interpolation in 20 tomographic data sets. Results show that the proposed method statistically significantly outperforms both linear and shape-based interpolation.     

Shape-based interpolation of multidimensional objects

A shape-based interpolation scheme for multidimensional images is presented. This scheme consists of first segmenting the given image data into a binary image, converting the binary image back into a gray image wherein the gray value of a point represents its shortest distance (positive value for points of the object and negative for those outside) from the cross-sectional boundary, and then interpolating the gray image. The set of all points with nonnegative values associated with them in the interpolated image constitutes the interpolated object. The method not only minimizes user involvement in interactive segmentation, but also leads to more accurate representation and depiction of dynamic as well as static objects. The general methodology and the implementation details of the method are presented and compared on a qualitative and quantitative basis to the existing methods. The generality of the proposed scheme is illustrated with a number of medical imaging examples.     

Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow

This paper proposes a new video super-resolution method based on feature-guided variational optical flow. The key-frames are automatically selected and super-resolved using a method based on sparse regression. To overcome the blocking artifacts and deal with the case of small structures with large displacement, an efficient method based on feature-guided variational optical flow is used to super-resolve the non-key-frames. Experimental results show that the proposed method outperforms the existing benchmark in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The average PSNR improvement from the bi-cubic interpolation is 7.15 dB for four datasets. Thanks to the flexibility of designed automatic key-frame selection and the validness of feature-guided variational optical flow, the proposed method is applicable to various practical video sequences.     

Block-level parallel processing for scaling evenly divisible images

Image scaling is a frequent operation in medical image processing. This paper presents how two-dimensional (2-D) image scaling can be accelerated with a new coarse-grained parallel processing method. The method is based on evenly divisible image sizes which is, in practice, the case with most medical images. In the proposed method, the image is divided into slices and all the slices are scaled in parallel. The complexity of the method is examined with two parallel architectures while considering memory consumption and data throughput. Several scaling functions can be handled with these generic architectures including linear, cubic B-spline, cubic, Lagrange, Gaussian, and sinc interpolations. Parallelism can be adjusted independent of the complexity of the computational units. The most promising architecture is implemented as a simulation model and the hardware resources as well as the performance are evaluated. All the significant resources are shown to be linearly proportional to the parallelization factor. With contemporary programmable logic, real-time scaling is achievable with large resolution 2-D images and a good quality interpolation. The proposed block-level scaling is also shown to increase software scaling performance over four times.     

基于整数小波变换的Wiener插值算法

小波变换中高低分辨率子带之间的相似性使得利用小波变换进行图像插值的方法成为可能.根据数字图像信号的特点,分析了小波变化后各个子带信号的特点,提出了基于整数小波变换的Wiener插值算法.用Wiener自适应滤波器训练得到插值滤波系数,同时结合既符合图像性质又能减小运算量的整数双正交小波基对图像插值.结果得到较高的信噪比和较好的主观视觉效果.平均峰值信噪比比传统的双线性插值法提升了2.4dB.     

一种学习向量神经网络的图像插值算法

利用一种新型学习向量神经网络实现了对灰度图像的基于最佳点对匹配的图像插值。采用新型学习向量神经网络的最佳点对匹配图像插值算法插值出的中间图像,较好的解决了插值图像边缘模糊的现象。试验结果表明,该方法插值得到的图像边界清晰较好,模糊度小,图像连续。     

Shape-based averaging.

A new method for averaging multidimensional images is presented, which is based on signed Euclidean distance maps computed for each of the pixel values. We refer to the algorithm as "shape-based averaging" (SBA) because of its similarity to Raya and Udupa's shape-based interpolation method. The new method does not introduce pixel intensities that were not present in the input data, which makes it suitable for averaging nonnumerical data such as label maps (segmentations). Using segmented human brain magnetic resonance images, SBA is compared to label voting for the purpose of averaging image segmentations in a multiclassifier fashion. SBA, on average, performed as well as label voting in terms of recognition rates of the averaged segmentations. SBA produced more regular and contiguous structures with less fragmentation than did label voting. SBA also was more robust for small numbers of atlases and for low atlas resolutions, in particular, when combined with shape-based interpolation. We conclude that SBA improves the contiguity and accuracy of averaged image segmentations.     

A new method for registration-based medical image interpolation

A new technique is presented for interpolating between grey-scale images in a medical data set. Registration between neighboring slices is achieved with a modified control grid interpolation algorithm that selectively accepts displacement field updates in a manner optimized for performance. A cubic interpolator is then applied to pixel intensities correlated by the displacement fields. Special considerations are made for efficiency, interpolation quality, and compression in the implementation of the algorithm. Experimental results show that the new method achieves good quality, while offering dramatic improvement in efficiency relative to the best competing method.     

Shape-based interpolation of multidimensional grey-level images

Shape-based interpolation as applied to binary images causes the interpolation process to be influenced by the shape of the object. It accomplishes this by first applying a distance transform to the data. This results in the creation of a grey-level data set in which the value at each point represents the minimum distance from that point to the surface of the object. (By convention, points inside the object are assigned positive values; points outside are assigned negative values.) This distance transformed data set is then interpolated using linear or higher-order interpolation and is then thresholded at a distance value of zero to produce the interpolated binary data set. Here, the authors describe a new method that extends shape-based interpolation to grey-level input data sets. This generalization consists of first lifting the n-dimensional (n-D) image data to represent it as a surface, or equivalently as a binary image, in an (n+1)-dimensional [(n+1)-D] space. The binary shape-based method is then applied to this image to create an (n+1)-D binary interpolated image. Finally, this image is collapsed (inverse of lifting) to create the n-D interpolated grey-level data set. The authors have conducted several evaluation studies involving patient computed tomography (CT) and magnetic resonance (MR) data as well as mathematical phantoms. They all indicate that the new method produces more accurate results than commonly used grey-level linear interpolation methods, although at the cost of increased computation.     

Surface interpolation from sparse cross sections using region correspondence

The ability to estimate a surface from a set of cross sections allows calculation of the enclosed volume and the display of the surface in three-dimensions. This process has increasingly been used to derive useful information from medical data. However, extracting the cross sections (segmenting) can be very difficult, and automatic segmentation methods are not sufficiently robust to handle all situations. Hence, it is an advantage if the surface reconstruction algorithm can work effectively on a small number of cross sections. In addition, cross sections of medical data are often quite complex. Shape-based interpolation is a simple and elegant solution to this problem, although it has known limitations when handling complex shapes. In this paper, the shape-based interpolation paradigm is extended to interpolate a surface through sparse, complex cross sections, providing a significant improvement over our previously published maximal disc-guided interpolation. The performance of this algorithm is demonstrated on various types of medical data (X-ray computed tomography, magnetic resonance imaging and three-dimensional ultrasound). Although the correspondence problem in general remains unsolved, it is demonstrated that correct surfaces can be estimated from a limited amount of real data, through the use of region rather than object correspondence.     

Wavelet-based Interpolation Scheme for Resolution Enhancement of Medical Images

A novel interpolation method for resolution enhancement is proposed in this paper. This method estimates wavelet coefficients in the high frequency subbands from a low resolution image using our proposed filters. An inverse wavelet transform is then performed for synthesis of a higher resolution image. Experimental results show that our proposed method outperforms other commonly used schemes objectively and subjectively. In addition, the processing time required in an algorithm-implemented Digital Signal Processor (DSP) is satisfied. By using the DSP hardware platform, off-line interpolation processing becomes easier and more feasible. The interpolated image has merits of high contrast and remarkable sharpness which essentially meet the requirements for interpolation of medical images. Our method can provide better quality of interpolated medical images compared to other methods to assist physicians in making diagnoses.     

A bivariate rational interpolation based on scattered data on parallel lines

In many practical problems, such as geological exploration, forging technology and medical imaging, among others, it has been detected that the scattered data are usually arranged in parallel lines. In this paper, a new approach to construct a bivariate rational interpolation over triangulation is presented, based on scattered data in parallel lines. The main advantage of this method comparing with the present interpolation methods have two points: (1) the interpolation function is carried out by a simple and explicit mathematical representation through the parameter α; (2) the shape of the interpolating surface can be modified by using the parameter for the unchanged interpolating data. Moreover, a local shape control method is employed to control the shape of surfaces. In the special case, the method of “Barycenter Value Control” is studied, and numerical examples are presented to show the performance of the method.     

Joint demosaicing and denoising.

The output image of a digital camera is subject to a severe degradation due to noise in the image sensor. This paper proposes a novel technique to combine demosaicing and denoising procedures systematically into a single operation by exploiting their obvious similarities. We first design a filter as if we are optimally estimating a pixel value from a noisy single-color (sensor) image. With additional constraints, we show that the same filter coefficients are appropriate for color filter array interpolation (demosaicing) given noisy sensor data. The proposed technique can combine many existing denoising algorithms with the demosaicing operation. In this paper, a total least squares denoising method is used to demonstrate the concept. The algorithm is tested on color images with pseudorandom noise and on raw sensor data from a real CMOS digital camera that we calibrated. The experimental results confirm that the proposed method suppresses noise (CMOS/CCD image sensor noise model) while effectively interpolating the missing pixel components, demonstrating a significant improvement in image quality when compared to treating demosaicing and denoising problems independently.     

Matching of tomographic slices for interpolation

An automatic method that can transform a sequence of tomographic image slices into an isotropic volume data set is described. In this method, correspondence is established between points in consecutive slices, and then this correspondence is used to estimate data between the slices by linear interpolation. The method takes advantage of the fact that consecutive slices have small geometric differences, and carries out the search in predicted small neighborhoods. Only points with high gradient magnitudes are used in the search process to increase the reliability of the correspondences. Mismatches that occur are detected and corrected using the continuity constraint in the correspondences. Experimental results showing the matching and interpolation of magnetic resonance slices and computed tomography slices are presented.     

一种加窗插值FFT谐波分析方法

由于很难实现同步采样和整周期截断,因此,利用FFT算法分析电网谐波信号时存在频谱泄露和栅栏效应,影响算法的分析精度。加窗插值FFT是抑制频谱泄露和消除栅栏效应的有效方法,在此提出一种基于3项3阶Nuttall窗插值FFT的谐波分析方法,推导了插值系数公式以及各次谐波的频率、幅值和相位的修正公式。对该算法与Hanning窗、Blackman窗插值FFT算法进行Matlab仿真对比研究,验证了该算法具有更高的分析精度。     

A level-set approach to image blending

This paper presents a novel method for blending images. Image blending refers to the process of creating a set of discrete samples of a continuous, one-parameter family of images that connects a pair of input images. Image blending has uses in a variety of computer graphics and image processing applications. In particular, it ran be used for image morphing, which is a method for creating video streams that depict transformations of objects in scenes based solely on pairs of images and sets of user-defined fiducial points. Image blending also has applications for video compression and image-based rendering. The proposed method for image blending relies on the progressive minimization of a difference metric which compares the level sets between two images. This strategy results in an image blend which is the solution of a pair of coupled, nonlinear, first-order partial differential equations that model multidimensional level-set propagations. When compared to interpolation this method produces more natural appearances of motion because it manipulates the shapes of image contours rather than simply interpolating intensity values. This strategy results in a process that has the qualitative property of deforming greyscale objects in images rather than producing a simple fade from one object to another. This paper presents the mathematics that underlie this new method, a numerical implementation, and results on real images that demonstrate its effectiveness.     

A morphology-based approach for interslice interpolation of anatomical slices from volumetric images

This paper proposes a new morphology-based approach for the interslice interpolation of current transformer (CT) and MRI datasets composed of parallel slices. Our approach is object based and accepts as input data binary slices belonging to the same anatomical structure. Such slices may contain one or more regions, since topological changes between two adjacent slices may occur. Our approach handles explicitly interslice topology changes by decomposing a many-to-many correspondence into three fundamental cases: one-to-one, one-to-many, and zero-to-one correspondences. The proposed interpolation process is iterative. One iteration of this process computes a transition sequence between a pair of corresponding input slices, and selects the element located at equal distance from the input slices. This algorithmic design yields a gradual, smooth change of shape between the input slices. Therefore, the main contribution of our approach is its ability to interpolate between two anatomic shapes by creating a smooth, gradual change of shape, and without generating over-smoothed interpolated shapes.      

Multi-epipolar lines matching-based ray-space interpolation for Free Viewpoint Video system

Ray-space based arbitrary viewpoint rendering without complex object segmentation or model construction is the main technology to realize Free Viewpoint Video （FVV） system for complex scenes. Ray-space interpolation and compression are two key techniques for the solution. In this paper, correlation among multiple epipolar lines in ray-space data is analyzed, and a new method of ray-space interpolation with multi-epipolar lines matching is proposed. Comparing with the pixel-based matching interpolation method and the block-based matching interpolation method, the proposed method can achieve higher Peak Signal to Noise Ratio （PSNR） in interpolating ray- space data and rendering arbitrary viewpoint images.