基于内容的医学图像检索技术

建立一个高效、准确的医学图像检索系统是目前具有挑战性的任务.文中在简述基于内容的医学图像检索模型后,着重探讨和分析了检索模型中图像特征提取、特征选择与融合、相似度匹配、图像预过滤、相关反馈等核心技术和相关算法,以及一些最新的应用.最后对基于内容的医学图像检索技术进行了展望.     

Perceptual Organization Based Computational Model for Robust Segmentation of Moving Objects

The role of perceptual organization in motion analysis has heretofore been minimal. In this work we present a simple but powerful computational model and associated algorithms based on the use of perceptual organizational principles, such as temporal coherence (or common fate) and spatial proximity, for motion segmentation. The computational model does not use the traditional frame by frame motion analysis; rather it treats an image sequence as a single 3D spatio-temporal volume. It endeavors to find organizations in this volume of data over three levels—signal, primitive, and structural. The signal level is concerned with detecting individual image pixels that are probably part of a moving object. The primitive level groups these individual pixels into planar patches, which we call the temporal envelopes. Compositions of these temporal envelopes describe the spatio-temporal surfaces that result from object motion. At the structural level, we detect these compositions of temporal envelopes by utilizing the structure and organization among them. The algorithms employed to realize the computational model include 3D edge detection, Hough transformation, and graph based methods to group the temporal envelopes based on Gestalt principles. The significance of the Gestalt relationships between any two temporal envelopes is expressed in probabilistic terms. One of the attractive features of the adopted algorithm is that it does not require the detection of special 2D features or the tracking of these features across frames. We demonstrate that even with simple grouping strategies, we can easily handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of training and specific object or illumination models. We present results on a large variety of motion sequences to demonstrate this robustness.     

A Continuous Probabilistic Framework for Image Matching

In this paper we describe a probabilistic image matching scheme in which the image representation is continuous and the similarity measure and distance computation are also defined in the continuous domain. Each image is first represented as a Gaussian mixture distribution and images are compared and matched via a probabilistic measure of similarity between distributions. A common probabilistic and continuous framework is applied to the representation as well as the matching process, ensuring an overall system that is theoretically appealing. Matching results are investigated and the application to an image retrieval system is demonstrated.     

SVM用于基于内容的自然图像分类和检索

在传统的基于内容图像检索的方法中，由于图像的领域较宽，图像的低级视觉特征和高级概念之间存在着较大的语义间隔，导致检索效果不佳．该文认为更有现实意义的做法是，缩窄图像的领域以减小低级特征和高级概念间的语义间隔，并利用机器学习方法自动建立图像类的模型，从而提供用户概念化的图像查询方式．该文以自然图像领域为例，使用支持向量机(SVM)学习自然图像的类别，学习到的模型用于自然图像分类和检索．实验结果表明作者的方法是可行的．     

基于内容的图象检索系统的设计与实现

依据当前对图象查询的要求,本文设计了一套完整的基于内容的图象信息检索系统,该系统较以往的各种系统,功能更加全面。对基于内容的图象信息检索算法作了研究．重点阐述了对颜色、边缘、纹理等全局特征的提取与匹配算法。实验结果表明,该系统能有效、快速地检索大规模的图象数据库,具有一定的应用价值。     

Optimal Speed-Up Parallel Image Template Matching Algorithms on Processor Arrays with a Reconfigurable Bus System,

The image template matching problem is one of the fundamental problems of and has many practical applications in image processing, pattern recognition, and computer vision. It is a useful operation for filtering, edge detection, image registration, and object detection [13]. In this paper, we first design twoO[(M²/p²)log logM] andO[(M²/p²)+(M/p)log logp] time parallel image template matching algorithms on a 3-D processor array with a reconfigurable bus system usingp²N²processors with each processor containingO(1) andO(M/p) restricted memory for 1 ≤p≤M≤N, respectively, for anN×Ndigital image and anM×Mtemplate. By increasing the number of processors, these two proposed algorithms can be run inO(M²/p²) time for speeding up the time complexity usingp²M^1/cN²andp^2+1/cN²processors, respectively, wherecis a constant andc≥1. Furthermore, anO(1) time can be also obtained from these two proposed algorithms by usingM²+1^/cN²processors. These results improve the best known bounds and achieve both optimal and optimal speed-up in their time and processor complexities.     

Gray-Level Reduction Using Local Spatial Features

This paper proposes a new method for reduction of the number of gray-levels in an image. The proposed approach achieves gray-level reduction using both the image gray-levels and additional local spatial features. Both gray-level and local feature values feed a self-organized neural network classifier. After training, the neurons of the output competition layer of the SOFM define the gray-level classes. The final image has not only the dominant image gray-levels, but also has a texture approaching the image local characteristics used. To split the initial classes further, the proposed technique can be used in an adaptive mode. To speed up the entire multithresholding algorithm and reduce memory requirements, a fractal scanning subsampling technique is adopted. The method is applicable to any type of gray-level image and can be easily modified to accommodate any type of spatial characteristic. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.     

Tracking of Human Limbs by Multiocular Vision

This article proposes a method for the tracking of human limbs from multiocular sequences of perspective images. These limbs and the associated articulations must first be modelled. During the learning stage, we model the texture linked to the limbs. The lack of characteristic points on the skin is compensated by the wearing of nonrepetitive texture tights. The principle of the method is based on the interpretation of image textured patterns as the 3D perspective projections of points of the textured articulated model. An iterative Levenberg–Marquardt process is used to compute the model pose in accordance with the analyzed image. The calculated attitude is filtered (Kalman filter) to predict the model pose in the following image of the sequence. The image patterns are extracted locally according to the textured articulated model in the predicted attitude. Tracking experiments, illustrated in this paper by cycling sequences, demonstrate the validity of the approach.     

Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach

This paper presents a general information-theoretic approach for obtaining lower bounds on the number of examples required for Probably Approximately Correct (PAC) learning in the presence of noise. This approach deals directly with the fundamental information quantities, avoiding a Bayesian analysis. The technique is applied to several different models, illustrating its generality and power. The resulting bounds add logarithmic factors to (or improve the constants in) previously known lower bounds.     

采用形态学边界特征的医学图像检索

特征提取是基于内容的图像检索(CBIR)中的关键步骤,如何有效提取反映高层语义的图像特征对于医学图像的检索至关重要.提出一种基于边界形状特征的医学图像检索方法.该方法首先通过多尺度形态学方法检测图像边界点,然后对边界图像进行形状特征提取,构建边界的形状密度直方图,最后通过相似性匹配实现医学图像检索.实验结果证明了所提取的边界形状特征在医学图像检索中的有效性,通过对比实验给出了结果分析和进一步的研究思路.     

基于内容的图像检索技术

随着多媒体技术的迅速发展,传统的基于关键字的信息检索技术已逐渐不能满足要求,因此,基于内容的图像检索技术成为当今的一个研究热点。介绍了基于内容图像检索系统的基本组成。综述了基于颜色、纹理、形状、语义等图像检索技术的主要方法。分析和比较了现有的各种图像检索技术的方法。讨论了相关反馈技术、检索性能的评价等CBIR研究中的关键问题。同时指出了CBIR研究中存在的问题,以及未来的发展趋势和研究方向。     

Optimal Deterministic Protocols for Mobile Robots on a Grid

This paper studies a system of m robots operating in a set of n work locations connected by aisles in a × grid, where m≤n. From time to time the robots need to move along the aisles, in order to visit disjoint sets of locations. The movement of the robots must comply with the following constraints: (1) no two robots can collide at a grid node or traverse a grid edge at the same time; (2) a robot's sensory capability is limited to detecting the presence of another robot at a neighboring node. We present a deterministic protocol that, for any small constant ε>0, allows m≤(1-ε)n robots to visit their target locations in O( ) time, where each robot visits no more than d≤n targets and no target is visited by more than one robot. We also prove a lower bound showing that our protocol is optimal. Prior to this paper, no optimal protocols were known for d>1. For d=1, optimal protocols were known only for m≤ , while for general m≤n only a suboptimal randomized protocol was known.     

A Region-Based Scheme Using RKLT and Predictive Classified Vector Quantization

This paper proposes a compression scheme for face profile images based on three stages, modelling, transformation, and the partially predictive classified vector quantization (CVQ) stage. The modelling stage employs deformable templates in the localisation of salient features of face images and in the normalization of the image content. The second stage uses a dictionary of feature-bases trained for profile face images to diagonalize the image blocks. At this stage, all normalized training and test images are spatially clustered (objectively) into four subregions according to their energy content, and the residuals of the most important clusters are further clustered (subjectively) in the spectral domain, to exploit spectral redundancies. The feature-basis functions are established with the region-based Karhunen–Loeve transform (RKLT) of clustered image blocks. Each image block is matched with a representative of near-best basis functions. A predictive approach is employed for mid-energy clusters, in both stages of search for a basis and for a codeword from the range of its cluster. The proposed scheme employs one stage of a cascaded region-based KLT-SVD and CVQ complex, followed by residual VQ stages for subjectively important regions. The first dictionary of feature-bases is dedicated to the main content of the image and the second is dedicated to the residuals. The proposed scheme is experimented in a set of human face images.     

3-D Reconstruction of Urban Scenes from Aerial Stereo Imagery: A Focusing Strategy

A contribution to the automatic 3-D reconstruction of complex urban scenes from aerial stereo pairs is proposed. It consists of segmenting the scene into two different kinds of components: the ground and the above-ground objects. The above-ground objects are classified either as buildings or as vegetation. The idea is to define appropriate regions of interest in order to achieve a relevant 3-D reconstruction. For that purpose, a digital elevation model of the scene is first computed and segmented into above-ground regions using a Markov random field model. Then a radiometric analysis is used to classify above-ground regions as building or vegetation, leading to the determination of the final above-ground objects. The originality of the method is its ability to cope with extended above-ground areas, even in case of a sloping ground surface. This characteristic is necessary in a urban environment. Results are very robust to image and scene variability, and they enable the utilization of appropriate local 3-D reconstruction algorithms.     

Optimal Series-Parallel Trade-offs for Reducing a Function to Its Own Graph

Consider the problem of computing a function given only an oracle for its graph. For this problem, we present optimal trade-offs between serial and parallel queries. In particular, we give a function for which parallel access to its own graph is exponentially more expensive than sequential access.     

Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model

The use of hypothesis verification is recurrent in the model-based recognition literature. Verification consists in measuring how many model features transformed by a pose coincide with some image features. When data involved in the computation of the pose are noisy, the pose is inaccurate and difficult to verify, especially when the objects are partially occluded. To address this problem, the noise in image features is modeled by a Gaussian distribution. A probabilistic framework allows the evaluation of the probability of a matching, knowing that the pose belongs to a rectangular volume of the pose space. It involves quadratic programming, if the transformation is affine. This matching probability is used in an algorithm computing the best pose. It consists in a recursive multiresolution exploration of the pose space, discarding outliers in the match data while the search is progressing. Numerous experimental results are described. They consist of 2D and 3D recognition experiments using the proposed algorithm.     

基于LFT算子的医学图像检索方法

在医学信息领域,对医学图像物理特征的研究有助于实现图像的自动分析和基于内容的检索.为此,本文提出了一种基于LFT算子的医学图像检索方法.首先提取图像的LFT直方图特征,此特征能够有效地降低图像特征维数,然后使用该特征进行基于内容的图像检索.实验表明,该方法具有较高的检准率和检全率,并且检索时间较短.     

Model-Based Object Recognition Using Geometric Invariants of Points and Lines

In this paper, we derive new geometric invariants for structured 3D points and lines from single image under projective transform, and we propose a novel model-based 3D object recognition algorithm using them. Based on the matrix representation of the transformation between space features (points and lines) and the corresponding projected image features, new geometric invariants are derived via the determinant ratio technique. First, an invariant for six points on two adjacent planes is derived, which is shown to be equivalent to Zhu's result [1], but in simpler formulation. Then, two new geometric invariants for structured lines are investigated: one for five lines on two adjacent planes and the other for six lines on four planes. By using the derived invariants, a novel 3D object recognition algorithm is developed, in which a hashing technique with thresholds and multiple invariants for a model are employed to overcome the over-invariant and false alarm problems. Simulation results on real images show that the derived invariants remain stable even in a noisy environment, and the proposed 3D object recognition algorithm is quite robust and accurate.