In this paper, we propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. A new informationtheoretic approach is presented for fi nding the pose of an object in an image. We want to find a linear transform matrix to minimize mutual information, or, equivalently, to maximize negentropy under. Alignment by maximization of mutual information ieee. More than ten years later, mutual information is now found at the core of many stateoftheart image registration algorithms 2. A voxel of the reference volume is denoted ux, where xare the coordinates of the voxel. Previous image registration schemes based on mutual information use shannons entropy measure, and they have been successfully applied for mono and multimodality registration. Mutual in formation mi is a basic concept from information theory, that is applied in the context of image registration to.
A new informationtheoretic approach is presented for finding the registration of volumetric medical images of differing modalities. In this section, we describe the maximization of mi for multimodal image registration. Automatic time sequence alignment in contrast enhanced mri by maximization of mutual information. Rigid point feature registration using mutual information 15 figure 14. This is an implementation and demonstration of registration by maximization of mutual information. The method is based on a formulation of the mutual information between the model and the image. Contributed article graph matching vs mutual information maximization for object detectionq ladan b. Alignment by maximization of mutual information 1 introduction. Robust image registration based on mutual information. The proposed approach is robust, realtime and gives.
Crosscorrelation, meansquare difference and ratio image uniformity are commonly used for registration of images of the same modality. Abstract a new information theoretic approach is presented for nding the pose of an object in an image. Medical image registration using mutual information. Technical report 1548 alignment by maximization of mutual. Multimodal volume registration by maximization of mutual. Inferring interaction partners from protein sequences using. However, conventional mutual information does not consider spatial dependency between adjacent voxels in images, thus reducing its efficacy as a similarity measure in image registration.
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. Alignment by maximization of mutual information abstract. Ieee trans on image processing, 2012 1 second order. In this paper, we propose a novel information theoretic temporal alignment method based on statistical dependence maximization. There are cases, however, where maximization of mutual information does not lead to the correct spatial alignment of. Mutual information mi was invented by shannon 15, and popularized in computer vision by viola and wells 17. And second, local and global mutual information maximization is introduced, allowing for representations that contain locallyconsistent and intraclass shared information across structural locations in an image. Large histogram computation for normalized mutual information on gpu sophie voisin devin a. Information theoretic similarity measures for image. Bradyb, stefan schaalc,d acalifornia institute of technology, computation and neural systems, division for biology, mc 974, pasadena, ca 92215, usa b3m corporate research laboratories, 3m center, building 2353f08, st. We first define terms and notation used in this work. Softassignbased alignment forslices 372, 422, 472, 572, 621 and 672 aligned with slice 522 from top left to bottom right 10 20 30 40 50 60 70 80 50 100 150 200 250 300 slice 572 points sampled at 4.
Multimodal registration via mutual information incorporating. The university of texas at arlington xian jiaotong university microsoft tencent 0 share. Digitally reconstructed radiographs from the treatment planning computed. Alignment by maximization of mutual information ieee conference. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. Ecse 626 project report multimodality image registration. Our method, which we call leastsquares dynamic time warping lsdtw, employs a squaredloss variant of mutual information called squaredloss mutual information smi as a dependency measure. Application to stereo tracking of humans stephen krotosky and mohan trivedi abstract this chapter introduces and analyzes a method for registering multimodal images with occluding objects in the scene. Weighted and deterministic entropy measure for image. In probability theory and information theory, the mutual information mi of two random variables is a measure of the mutual dependence between the two variables.
Pdf automatic time sequence alignment in contrast enhanced. A new informationtheoretic approach is presented for finding the pose of an object in an image. Each lung cancer patient was aligned using assisted alignment without manual alignment intervention. Alignment by maximization of mutual information conference paper pdf available in international journal of computer vision 242. An overview of mutual information mi based scan alignment framework. A pairing score based on pointwise mutual information pmi consider an alignment of m concatenated sequences ab of length l, where a is a protein from family and b is a protein from family. Word alignment and the expectationmaximization algorithm. We present some new results on the nonparametric estimation of entropy and mutual information. Aligning sequences and actions by maximizing space time. Second order optimization of mutual information for realtime image registration amaury dame, eric marchand abstractin this paper we present a direct image registration approach that uses mutual information mi as a metric for alignment. Estimation of entropy and mutual information 1195 ducing anything particularly novel, but merely formalizing what statisticians have been doing naturally since well before shannon wrote his papers. A new information theoretic approach is presented for finding the pose of an object in an image.
Threedimensional registration was carried out using the classical maximization of mutual information 44, 45 see ctslices in fig. The second term is the entropy of the part of the test volume into which the reference volume projects. Bradyb, stefan schaalc,d acalifornia institute of technology, computation and neural systems, division for biology, mc 974, pasadena, ca 92215, usa. The aim of this project is to demonstrate its simplicity, accuracy, and robustness, by implementing the original algo. Our method, called maximum likelihood mutual information mlmi, has several at. Visual correspondence using energy minimization and mutual. Spatial mutual information as similarity measure for 3d. The recent introduction of the criterion of maximization of mutual information, a basic concept from information theory, has proven to be a breakthrough in the field. Mutual information aspects of scale space images department of.
If the mutual information of a set of variables is decreased indicating the variables are less dependent then the negentropy will be increased, and are less gaussian. It works well in domains where edge or gradientmagnitude based methods have difficulty, yet it is more robust than traditional correlation. Results of combining both standard mutual information as well a normalized measure are presented for rigid registration of threedimensional clinical images mr, ct and pet. Inferring interaction partners from protein sequences.
Robust and fast 3d scan alignment using mutual information. Pdf alignment by maximization of mutual information. A method to assign photographic plates to corresponding color channels is also included. It works well in domains where edge or gradientmagnitude based methods have difficul. The gold standard was the average of the 9 manual observations as mentioned previously. Submitted to the department of electrical engineering and computer science on. In this work we concentrate on maximization of mutual information between the two images as the basic criteria for registration. In our derivation few assumptions are made about the nature of the imaging process. Validation of manual and assisted alignment techniques. As applied here the technique is intensitybased rather than featurebased.
The technique does not require information about the surfa. Alignment by maximization of mutual information citeseerx. By contrast, we focus on removing out the restriction of readout function and arriving at graphical mutual information maximization in a nodelevel by directly maximizing mi between inputs and outputs of the encoder. Experi mental results involving mrict and mripet registration are reported in section 3. Section 5 includes an analysis of an idealized multimodal registration problem. Graph representation learning via graphical mutual.
It has been primarily used for registration problems, where the goal is to. Closer points are rendered brighter than more distant ones. The method is based on a formulation of the mutual information between the model and the image called emma. To solve the maximization problem, the assumption made in the differential. Interpolation artefacts in mutual informationbased image registration. As applied here the technique is intensitybased, rather than featurebased. In image registration mutual information is a wellperforming measure based on principles of. William m wells iii alignment by maximization of mutual information this talk will summarize the historical emergence of the mutual information mi approach to image registration. The maximisation of information transmission over noisy channels is a common, albeit generally computationally di. Alignment by maximization of mutual information springerlink. A voxel of the test volume is denoted similarly as vx. Rigid point feature registration using mutual information.
Graph representation learning via graphical mutual information maximization. There are cases, however, where maximization of mutual information does not lead to the correct spatial alignment of a pair of images. Dependence maximizing temporal alignment via squaredloss. It encourages transformations that project u into complex parts of v. Mutual information of words is often used as a significance function for the computation of collocations in corpus linguistics. This strategy bears a striking resemblance to regularization methods employed in abstract statistical inference grenander, 1981, generally known. Pdf medical image registration using mutual information. Second order optimization of mutual information for realtime image. Alignment by maximization of mutual information by. Because the traditional mutual information similarity measure ignores the. Index termsmatching criterion, multimodality images, mu tual information, registration. Chapter 14 registering multimodal imagery with occluding. Mutual information computation and maximization using gpu, ieee.
Registering multimodal imagery with occluding objects using mutual information. Mi maximization is applied over the voxelizedfeatures computed from two partially overlapping scans. Mutual information is used in determining the similarity of two different clusterings of a dataset. Alignment by maximization of mutual information international journal of computer vision, 242 pg 7154, 1997 paul viola and william m. Maximization of mutual information the approach presented here could be paraphrased under the motto the brain has to process information, thus evolution will have taken care that it is as optimal in the sense of information theory as possible, roots back on the initiative of linsker 1986, 1988, 1989. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. On the right is a depth map of a model of rk that describes the distance to each of the visible points of the model.
Alignment by maximisation of mutual information microsoft. Information theoretic similarity measures for image registration and segmentation sunday 20th september 14. While solutions for intrapatient affine registration based on this concept are already commercially available, current research in the field focuses on interpatient nonrigid matching. A new informationtheoretic approach is presented for fi\fnding the pose of an object in an image. Word alignment and the expectation maximization algorithm adam lopez university of edinburgh the purpose of this tutorial is to give you an example of how to take a simple discrete probabilistic model and derive the expectation maximization updates for it and then turn them into code. Alignment by maximization of mutual information core. Multimodality image registration by maximization of mutual. Image registration by maximization of combined mutual. In our derivation, few assumptions are made about the nature of the imaging process.
Section 4 describes the use of our alignment technology to assist in. Abstract a new informationtheoretic approach is presented for nding the pose of an object in an image. Mutual information and its variant, normalized mutual information, are the most popular image similarity measures for registration of multimodality images. Dependence maximizing temporal alignment via squaredloss mutual information.
Word alignment and the expectationmaximization algorithm adam lopez university of edinburgh the purpose of this tutorial is to give you an example of how to take a simple discrete probabilistic model and derive the expectation maximization updates for it and then turn them into code. Viola this publication can be retrieved by anonymous ftp to publications. The registration is achieved if the maximum of the mutual information is attained. More specifically, it quantifies the amount of information in units such as shannons, commonly called bits obtained about one random variable through observing the other random variable. Contributed article graph matching vs mutual information. The method is based on a formulation of the mutual information between the model and the image called lemma. The resulting im algorithm is analagous to the em algorithm, yet max.
Wells, iii, alignment by maximization of mutual information, in international conference on computer visione. Alignment by maximization of mutual information abstract maximum 200 words a new information theoretic approach is presented for finding the pose of an object in an image the technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to onsof. Mutual information is useful in various data processing tasks such as feature selection or independent component analysis. Medical image analysis 1996 volume 1, number 1, pp 3551 c oxford university press multimodal volume registration by maximization of mutual information william m. Alignment by maximization of mutual information 9 figure 1. Wells, iii, alignment by maximization of mutual information. The technique does not require information about the surface properties of the object, besides its shape and is robust with respect to variations of illumination. As such, it provides some advantages over the traditional rand index.
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