线算子引导Gabor小波的视网膜血管分割

视网膜血管自动分割能辅助诊断某些眼底疾病和系统性血管疾病。为了提高血管自动分割的效率,因此提出了一种线算子引导Gabor小波的视网膜血管分割方法。利用线算子检测血管方向的最优匹配角,将其作为Gabor小波变换的旋转角构建4个不同尺度的Gabor小波,并提取4维Gabor小波特征,加上两个线强度和预处理后的图像灰度,构建7维特征向量,采用SVM进行分类。与其他基于Gabor小波的方法相比,本方法只需计算最优匹配角所对应方向的Gabor小波特征,大大降低了多尺度Gabor小波特征提取的计算量,此外线算子特征与Gabor小波特征的良好互补性,有利于提高血管与背景的辨别度。在DRIVE眼底数据库上进行实验,其平均准确率、灵敏度及特异性分别为0. 936 1、0. 823 8及0. 955 4,获得了不错的分割性能。     

Adaptive transforms for image coding using spatially varyingwavelet packets

We introduce a novel, adaptive image representation using spatially varying wavelet packets (WPs), Our adaptive representation uses the fast double-tree algorithm introduced previously (Herley et al., 1993) to optimize an operational rate-distortion (R-D) cost function, as is appropriate for the lossy image compression framework. This involves jointly determining which filter bank tree (WP frequency decomposition) to use, and when to change the filter bank tree (spatial segmentation). For optimality, the spatial and frequency segmentations must be done jointly, not sequentially. Due to computational complexity constraints, we consider quadtree spatial segmentations and binary WP frequency decompositions (corresponding to two-channel filter banks) for application to image coding. We present results verifying the usefulness and versatility of this adaptive representation for image coding using both a first-order entropy rate-measure-based coder as well as a powerful space-frequency quantization-based (SPQ-based) wavelet coder introduced by Xiong et al. (1993).     

An ensemble classification-based approach applied to retinal blood vessel segmentation

This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1?which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.     

Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation

It is well known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. We have previously shown this to be true for atlas-based segmentation of biomedical images. The conventional method for combining individual classifiers weights each classifier equally (vote or sum rule fusion). In this paper, we propose two methods that estimate the performances of the individual classifiers and combine the individual classifiers by weighting them according to their estimated performance. The two methods are multiclass extensions of an expectation-maximization (EM) algorithm for ground truth estimation of binary classification based on decisions of multiple experts (Warfield et al., 2004). The first method performs parameter estimation independently for each class with a subsequent integration step. The second method considers all classes simultaneously. We demonstrate the efficacy of these performance-based fusion methods by applying them to atlas-based segmentations of three-dimensional confocal microscopy images of bee brains. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of classifier combination methods in atlas-based segmentation. By applying random deformations, a given ground truth atlas is transformed into multiple segmentations that could result from imperfect registrations of an image to multiple atlas images. In a second evaluation study, multiple actual atlas-based segmentations are combined and their accuracies computed by comparing them to a manual segmentation. We demonstrate in both evaluation studies that segmentations produced by combining multiple individual registration-based segmentations are more accurate for the two classifier fusion methods we propose, which weight the individual classifiers according to their EM-based performance estimates, than for simple sum rule fusion, which weights each classifier equally.     

手指静脉图像血管网分形修复方法

由于生物组织对近红外光具有高散射效应，手指静脉红外透射成像质量往往较差，血管网络存在信息残缺。为了解决指静脉血管残缺问题，本文提出一种基于分形的手指静脉红外图像血管网络修复方法。首先，利用多尺度Gabor滤波对指静脉图像增强，进而提取血管骨架；其次，利用Gabor滤波得到的方向特征对血管网络进行结构预修复；接着采用K均值聚类方法提取指静脉结构的父子血管长度比特征，将其作为一种分形特征；然后，利用该特征计算缺损血管长度；最后，建立血管点移动模板，通过统计相邻血管点位置移动概率实现血管形态模拟及网络修复。实验结果表明，本文方法可以实现指静脉图像局部残缺区域的修复，从而提高手指静脉识别精度。      

Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.     

基于二维Gabor小波的人脸识别算法

该文提出了一种基于二维Gabor小波的人脸识别算法。该算法先对人脸图像进行多分辨率的Gabor小波变换,然后在图像上放置一组网格结点,每个结点用该结点处的多尺度Gabor幅度特征描述,采用主元分析法对每个结点进行去相关、降维,最后形成特征结。把每个特征结作为观测向量,对隐马尔可夫模型进行训练,并把优化的模型参数用于人脸识别。实验结果表明,该方法识别率高,复杂度较低。     

Ridge-based vessel segmentation in color images of the retina

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kappaNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.     

基于Gabor小波滤波器的红外图像多尺度识别

讨论了Ｇａｂｏｒ小波滤波器理论,并利用Ｇａｂｏｒ小波滤波器的多尺度特性进行了外图像识别,还进行了计算仿真实验,实验结果表明本文算法是可行的和有效的。     

Multiscale image segmentation using wavelet-domain hidden Markovmodels

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images without the need for decompression into the space domain. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.     

基于Gabor小波变换和PCA的人脸识别方法

基于Gabor小波在图像表征方面的优越性,阐述了将Gabor小波和主分量分析(PCA)相结合用于人脸识别的方案。对人脸图像进行Gabor小波变换,通过PCA(主分量分析)降维后,计算特征点之间的距离,最后进行人脸识别。     

多特征视觉Hash方案

提出一种基于认证安全性的视觉Hash设计方案.通过对视觉Hash认证中鲁棒性、认证集合相互关系的分析,指出了采用多特征构造视觉Hash的必要性.多特征方法町以平衡视觉Hash认证中的虚警、漏警问题.设计了一种基于小波分解的视觉Hash算法.对多次分解的小波低频系数进行量化提取多重特征,利用精确Hash算法组合生成认证Hash.实验结果表明本方案对JPEG压缩、滤波、噪声等处理有良好的鲁棒性,且具有较好的认证安全性,可以用于图像的真实性认证.     

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.     

White and black blood volumetric angiographic filtering:ellipsoidal scale-space approach

Prefiltering is a critical step in three-dimensional (3D) segmentation of a blood vessel and its display. This paper presents a scale-space approach for filtering white blood and black blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to isotropic volume followed by 3D higher order separable Gaussian derivative convolution with known scales to generate edge volume. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the nonvasculature and background structures yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) images. The system is run over 20 patient studies from different areas of the body such as the brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 s of processing time per study for a data size of 512 × 512 × 194, which includes the complete performance evaluation. We also compare our strategy with the recently published MR filtering algorithms by Alexander et al. (2000) and Sun et al. (1999)     

Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.     

Optimal Gabor filters for texture segmentation

Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs.     

Improved detection of the central reflex in retinal vessels using a generalized dual-gaussian model and robust hypothesis testing

This updates an earlier publication by the authors describing a robust framework for detecting vasculature in noisy retinal fundus images. We improved the handling of the "central reflex" phenomenon in which a vessel has a "hollow" appearance. This is particularly pronounced in dual-wavelength images acquired at 570 and 600 nm for retinal oximetry. It is prominent in the 600 nm images that are sensitive to the blood oxygen content. Improved segmentation of these vessels is needed to improve oximetry. We show that the use of a generalized dual-Gaussian model for the vessel intensity profile instead of the Gaussian yields a significant improvement. Our method can account for variations in the strength of the central reflex, the relative contrast, width, orientation, scale, and imaging noise. It also enables the classification of regular and central reflex vessels. The proposed method yielded a sensitivity of 72% compared to 38% by the algorithm of Can et al., and 60% by the robust detection based on a single-Gaussian model. The specificity for the methods were 95%, 97%, and 98%, respectively.     

基于Gabor小波变换的机载SAR海面风向反演方法

给出了一种基于Gabor小波变换的机载合成孔径雷达（SAR）海面风向反演的新方法。该方法利用Gabor小波对SAR图像进行二次小波分解,并对小波系数作FFT变换来获取图像谱,其低波数谱连线的垂线方向就是海面风场的风向。利用该方法获得了SAR图像海面风向信息,并与àtrous算法反演结果、浮标测得的海面风向（真值）进行了比较。实验结果表明,采用该方法获得的机载SAR海面风向反演结果与海面浮标实测数据吻合,比àtrous算法有较大改进。     

Authentication using Finger Knuckle Prints

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like the finger knuckle print (FKP) of a person is unique and secure. Finger knuckle print is a novel biometric trait and is not explored much for real-time implementation. In this paper, three different algorithms have been proposed based on this trait. The first approach uses Radon transform for feature extraction. Two levels of security are provided here and are based on eigenvalues and the peak points of the Radon graph. In the second approach, Gabor wavelet transform is used for extracting the features. Again, two levels of security are provided based on magnitude values of Gabor wavelet and the peak points of Gabor wavelet graph. The third approach is intended to authenticate a person even if there is a damage in finger knuckle position due to injury. The FKP image is divided into modules and module-wise feature matching is done for authentication. Performance of these algorithms was found to be much better than very few existing works. Moreover, the algorithms are designed so as to implement in real-time system with minimal changes.     

病变视网膜图像血管网络的自动分割

现有的视网膜血管分割方法大多只针对正常的视网膜图像进行分割,不能实现对发生病变的视网膜图像的分割.为此,提出了一种新的病变视网膜图像血管网络分割方法.该方法首先采用向量场散度方法获得病变视网膜图像中大部分血管的中心线,然后计算出中心线上各像素点的方向信息并采用改进的定向局部对比度方法检测出中心线两侧的血管像素,最后对获得的血管段末端进行反向外推追踪,分割出最终的血管网络.通过对通用的STARE眼底图像库中所有病变视网膜图像的实验仿真,结果表明本文算法获得了0.9426的ROC曲线面积和0.9502的准确率,算法性能明显优于Hoover算法和Benson等提出的算法.此外,本文算法还克服了Benson算法的局限性,对不同类型的病变视网膜图像都具有较好的鲁棒性.