Computer Science › Computer Vision and Pattern Recognition

Medical Image Segmentation Techniques

Works Count: 61146

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Description

This cluster of papers covers advances in image segmentation techniques, particularly focusing on medical image analysis, graph cuts, active contours, MRI segmentation, deformable image registration, level set methods, statistical shape models, deep learning, and texture analysis.

Keywords

Image Segmentation; Medical Image Analysis; Graph Cuts; Active Contours; MRI Segmentation; Deformable Image Registration; Level Set Methods; Statistical Shape Models; Deep Learning; Texture Analysis

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

2014-12-04

Bjoern Menze , András Jakab , Stefan Bauer +7 more

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art … In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

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A survey of thresholding techniques

1988-02-01

Prasanna K. Sahoo , Soheil Soltani , Alexander Wong

Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

2007-06-26

Brian Avants , Charles L. Epstein , M GROSSMAN +1 more

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A review on image segmentation techniques

1993-09-01

Nikhil R. Pal , Sankar K. Pal

A unified statistical approach for determining significant signals in images of cerebral activation

1996-01-01

Keith J. Worsley , Sean Marrett , P. Neelin +3 more

We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, … We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, chi(2) and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) and the usual uncorrected P-value at a single pixel or voxel.

Efficient Graph-Based Image Segmentation

2004-04-01

Pedro F. Felzenszwalb , Daniel P. Huttenlocher

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Whole Brain Segmentation

2002-01-01

Bruce Fischl , David H. Salat , Evelina Busa +7 more

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Accurate and robust brain image alignment using boundary-based registration

2009-07-01

Douglas N. Greve , Bruce Fischl

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Multimodality image registration by maximization of mutual information

1997-04-01

Frederik Maes , André Collignon , Dirk Vandermeulen +2 more

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, Mutual Information or relative entropy, as a new matching criterion.The … A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, Mutual Information or relative entropy, as a new matching criterion.The method presented in this paper applies Mutual Information to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned.Maximization of Mutual Information is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved.The accuracy of the mutual information criterion is validated for rigid body registration of CT, MR and PET images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation.Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction or other pre-processing steps, which makes this method very well suited for clinical applications.

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Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

2002-10-01

Mark Jenkinson

Statistical and structural approaches to texture

1979-01-01

R.M. Haralick

In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital … In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.

Spatial registration and normalization of images

1995-01-01

Karl Friston , John Ashburner , Chris Frith +3 more

Abstract This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This … Abstract This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss, Inc.

Mean shift: a robust approach toward feature space analysis

2002-05-01

Dorin Comaniciu , Peter Meer

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique … A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Fast robust automated brain extraction

2002-09-18

Stephen M. Smith

Abstract An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed. It is very robust and accurate and has been tested on thousands of … Abstract An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre‐processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods. Hum. Brain Mapping 17:143–155, 2002. © 2002 Wiley‐Liss, Inc.

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Seeded region growing

1994-06-01

R. Adams , Leanne Bischof

We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of … We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Medical image analysis: progress over two decades and the challenges ahead

2000-01-01

James S. Duncan , Nicholas Ayache

The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts … The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come.

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Contour Detection and Hierarchical Image Segmentation

2010-08-27

Pablo Arbeláez , Michael Maire , Charless C. Fowlkes +1 more

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues … This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

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Nonrigid registration using free-form deformations: application to breast MR images

1999-01-01

Daniel Rueckert , Luke Sonoda , Carmel Hayes +3 more

In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. … In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

Active contours without edges

2001-01-01

Tony F. Chan , Luminita A. Vese

We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our … We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.

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AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages

1996-06-01

Robert W. Cox

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Normalized cuts and image segmentation

2000-01-01

Jianbo Shi , Jitendra Malik

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at … We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging.

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Watersheds in digital spaces: an efficient algorithm based on immersion simulations

1991-06-01

Luc Vincent , Pierre Soille

A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine … A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

2006-03-21

Paul A. Yushkevich , Joseph Piven , Heather C. Hazlett +4 more

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Principal warps: thin-plate splines and the decomposition of deformations

1989-06-01

Fred L. Bookstein

The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current … The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Multiscale vessel enhancement filtering

1998-01-01

Alejandro F. Frangi , Wiro J. Niessen , Koen L. Vincken +1 more

The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of … The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter. A vesselness measure is obtained on the basis of all eigenvalues of the Hessian. This measure is tested on two dimensional DSA and three dimensional aortoiliac and cerebral MRA data. Its clinical utility is shown by the simultaneous noise and background suppression and vessel enhancement in maximum intensity projections and volumetric displays.

A pyramid approach to subpixel registration based on intensity

1998-01-01

P. Thévenaz , Urs E. Ruttimann , Michaël Unser

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes … We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.

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Survey over image thresholding techniques and quantitative performance evaluation

2004-01-01

B. Sankur

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging … The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.

elastix: A Toolbox for Intensity-Based Medical Image Registration

2009-11-23

Stefan Klein , Marius Staring , Keelin Murphy +2 more

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), … Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

A survey of medical image registration

1998-03-01

J. B. Antoine Maintz , Max A. Viergever

A reproducible evaluation of ANTs similarity metric performance in brain image registration

2010-09-18

Brian Avants , Nicholas J. Tustison , Gang Song +3 more

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Networks for approximation and learning

1990-01-01

Tomaso Poggio , Federico Girosi

The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class … The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class of three-layer networks called regularization networks are discussed. Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks. Learning as approximation and learning as hypersurface reconstruction are discussed. Two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering. The theory of regularization networks is generalized to a formulation that includes task-dependent clustering and dimensionality reduction. Applications of regularization networks are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

A Computational Approach to Edge Detection

1986-11-01

John Canny

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. … This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

Snakes, shapes, and gradient vector flow

1998-03-01

Chenyang Xu , Jerry L. Prince

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, … Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.

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Image registration methods: a survey

2003-09-12

Barbara Zitová , Jan Flusser

A global optimisation method for robust affine registration of brain images

2001-06-01

Mark Jenkinson , Stephen M. Smith

Shape modeling with front propagation: a level set approach

1995-01-01

R. Malladi , James A. Sethian , Baba C. Vemuri

Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new … Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and overcomes some of their limitations. The authors' techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of the authors' model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian (1988) to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. The authors present a variety of ways of computing the evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Scale-space and edge detection using anisotropic diffusion

1990-07-01

Pietro Perona , Jitendra Malik

A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such … A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Automatically Parcellating the Human Cerebral Cortex

2003-12-03

Bruce Fischl , André van der Kouwe , Christophe Destrieux +7 more

We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This … We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. This procedure incorporates both geometric information derived from the cortical model, and neuroanatomical convention, as found in the training set. The result is a complete labeling of cortical sulci and gyri. Examples are given from two different training sets generated using different neuroanatomical conventions, illustrating the flexibility of the algorithm. The technique is shown to be comparable in accuracy to manual labeling.

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A fast diffeomorphic image registration algorithm

2007-07-19

John Ashburner

Mutual-information-based registration of medical images: a survey

2003-07-29

Josien P. W. Pluim , J. B. Antoine Maintz , Max A. Viergever

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview … An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Comparing images using the Hausdorff distance

1993-01-01

D.P. Huttenlocher , G.A. Klanderman , W.J. Rucklidge

The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be … The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Image analysis and mathematical morphology

1982-09-01

Quantitative stereology

1972-02-01

G. Ondracek

Unified segmentation

2005-04-02

John Ashburner , Karl Friston

Image Analysis and Mathematical Morphology.

1983-06-01

P. J. Diggle , J. Serra

Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

2002-10-01

Mark Jenkinson , Peter Bannister , Michael Brady +1 more

On the joint use of simulations and experimental RX images for generating very large datasets with application to object detection tasks

2025-06-24

Anthony Touron , Roberto Miorelli , Julie Escoda +2 more | e-Journal of Nondestructive Testing

Machine Learning (ML) and Deep Learning (DL) algorithms are increasingly used in industrial domains to enhance accuracy and efficiency in error-prone tasks. Their performance hinges on data quantity and quality. … Machine Learning (ML) and Deep Learning (DL) algorithms are increasingly used in industrial domains to enhance accuracy and efficiency in error-prone tasks. Their performance hinges on data quantity and quality. In Nondestructive Testing and Evaluation (NdT&E), data scarcity, often due to confidentiality and rare events, poses a challenge. This is particularly true for Radiographic Testing (RT) data. To address this, we propose a systematic approach to combine simulated data with experimental radiographic (RX) images, creating large datasets with common defects (e.g., porosity, flaws) found in nuclear welding inspections. This process, called in this work Virtual Flaws (VFs) insertion, enables training ML algorithms without manual labelling or extensive experimental datasets. We evaluate the effectiveness of VFs-based datasets in object detection tasks using state-of-the-art algorithms.

Active Contour for Applying CT Segmentation on a Single Image to Surrounding Multiple Images

2025-06-24

Toshiki Kawamoto , Yutaka Ohtake | e-Journal of Nondestructive Testing

There is a growing interest in using X-ray CT scanners to measure industrial products, as CT scan data holds significant potential for reverse engineering and model-based design. To fully utilize … There is a growing interest in using X-ray CT scanners to measure industrial products, as CT scan data holds significant potential for reverse engineering and model-based design. To fully utilize CT scan data from assembled parts, it is essential to segment each individual component. The commonly used thresholding method is ineffective when there is no luminance difference at the boundaries between parts. Consequently, segmentation becomes particularly challenging when adjacent parts are made of the same material (i.e., have the same density). When threshold-based segmentation fails, manual interventions such as outlining objects with mouse clicks is often required. However, manual segmentation on a single image may lead to misalignments in surrounding slices, even though contour shapes generally remain similar within a certain region. If artificial intelligence can correct these misalignments and propagate contours across multiple slices, it could enable segmentation in many cases that are currently unfeasible with existing technologies. In this study, we propose a deep learning-based active contour algorithm for applying the manual segmentation performed on a single image to its surrounding slices.

Image Quality Analysis of a Laminographic Multiplicative Algebraic Reconstruction Algorithm compared to Filtered Shift Averaging

2025-06-24

Weslley Carlos Dias da Silva , Uwe Zscherpel , Uwe Ewert +1 more | e-Journal of Nondestructive Testing

Ionizing radiation imaging techniques are widely used in the industrial area for inspection of components and equipment. Among these techniques, Laminography has been used in situations where there is an … Ionizing radiation imaging techniques are widely used in the industrial area for inspection of components and equipment. Among these techniques, Laminography has been used in situations where there is an asymmetry of the object of interest or in situations where it is not possible to perform a complete rotation of the object or of the X-ray tube and detector around the object for Computed Tomography (CT). Typically, Laminography uses reconstruction algorithms producing volumetric information (3D solid) from a certain number of projections. Several reconstruction algorithms are described in the literature, namely: Tomosynthesis, modified Filtered Backprojection, Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstruction Technique (SIRT), Simultaneous Algebraic Reconstruction Technique (SART), Maximum Likelihood and Statistical Reconstruction. In this work, an analytical algorithm, called "Filtered Shifted Average" (FSA) and another iterative algorithm, called "Multiplicative Algebraic Reconstruction Technique" (MART) were optimized and used to perform coplanar translational laminographic reconstructions. Several aspects of image quality, such as analysis of Modulation Transfer Function (MTF), Contrast-to-Noise ratio (CNR), Noise and Detectability were measured to characterize and evaluate the relative performance between the FSA and MART techniques. All laminographic image volumes were obtained using the Coplanar Translational Laminography (CTL) arrangement. Several image acquisitions were made for an increasing number of projections (from 25 up to few thousands) in order to have a greater range of analysis and conclusions about the two algorithms used in this study. The results show that there is no significant dependence of the MTF10% value regarding the tested reconstruction algorithms and the number of projections. The MTF analysis is a suitable tool to describe the different filter properties of FSA and the optimized MART reconstruction. Furthermore, both CNR and the 1/Noise analysis showed higher values for all acquisition conditions with the MART algorithm in comparison with FSA. Regarding detectability, the differences between the algorithms were more accentuated for the condition with a low number of projections. The CTL was applied as one unidirectional scan and as a combination of two orthogonal planar scans, called cross coplanar translational laminography (C-CTL). Finally, there was a significant improvement in the contrast modulation for the images obtained with C-CTL, applying both algorithms for CTL. The choice and correct use of an iterative algorithm (MART) yields several advantages regarding the image quality, despite requiring more computational resources than the analytical algorithm (FSA).

Few-shot medical image segmentation via query transformation learning

2025-06-24

Shihao Zheng , Huisi Wu , Zhijian Gao +1 more | The Visual Computer

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta

2025-06-24

Hirohisa Oda , Mayu Wakamori , Toshiaki Akita | Radiological Physics and Technology

A Hyper-Heuristic Based Strategy for Image Segmentation Using Multilevel Thresholding

2025-06-21

| Deleted Journal

Low-level and high-level features co-directed weakly supervised instance segmentation for optical remote sensing image interpretation

2025-06-19

Honghua Zhao , Weiwen Wang , Xia Zou +2 more | International Journal of Remote Sensing

Nnet: N-type neural network for accurate segmentation of brain structures in MRI

2025-06-18

Xiufeng Zhang , Lingzhuo Tian , Yunfei Jiang +1 more | Biomedical Signal Processing and Control

Hybrid Landmark- and Intensity-Based Image Registration

2025-06-17

Mia Mojica , Mihaela Pop , Mehran Ebrahimi | Sensing and Imaging

Hyper-BTS: Brain tumor segmentation based on hypergraph guidance

2025-06-16

Qianren Guo , Yuehang Wang , Yongji Zhang +3 more | Pattern Recognition

Improving kidney segmentation in pathological images: a multiscale approach to resolve fragmentation and incomplete boundaries

2025-06-16

Muhammad Wajeeh Us Sima , Chengliang Wang , Muhammad Arshad +4 more | Journal of King Saud University - Computer and Information Sciences

Image-free single-pixel semantic segmentation for complex scene based on Multi-scale U-Net

2025-06-16

Tengfei Liu , Yanfeng Bai , Jianxia Chen +4 more | Chinese Physics B

Abstract Single-pixel imaging (SPI) receives widespread attention due to its superior anti-interference capabilities, and image segmentation technology can effectively facilitate its recognition and information extraction. However, the complexity of the … Abstract Single-pixel imaging (SPI) receives widespread attention due to its superior anti-interference capabilities, and image segmentation technology can effectively facilitate its recognition and information extraction. However, the complexity of the target scene and plenty of imaging time in SPI make it challenging to achieve high-quality and concise segmentation. In this paper, we investigate the image-free intricate scene semantic segmentation in SPI. Using “learned”illumination patterns allows for the full extraction of the object’s spatial information, thereby enabling pixel-level segmentation results through the decoding of the received measurements. Simulation and experimentation show that, in the absence of image reconstruction, the mean intersection over union (MIoU) of segmented image can reach higher than 85%, and the Dice coefficient (DICE) close to 90% even at the sampling ratio of 5%. Our approach may be favorable to applications in medical image segmentation and autonomous driving field.

LMS-Net: A learned Mumford-Shah network for binary few-shot medical image segmentation

2025-06-15

Shengdong Zhang , Fan Jia , Xiangdong Li +4 more | Medical Image Analysis

Ricci Curvature Tensor-Based Volumetric Segmentation

2025-06-15

Jisui Huang , Ke Chen , Andreas Alpers +1 more | International Journal of Computer Vision

Abstract Existing level set models employ regularization based only on gradient information, 1D curvature or 2D curvature. For 3D image segmentation, however, an appropriate curvature-based regularization should involve a well-defined … Abstract Existing level set models employ regularization based only on gradient information, 1D curvature or 2D curvature. For 3D image segmentation, however, an appropriate curvature-based regularization should involve a well-defined 3D curvature energy. This is the first paper to introduce a regularization energy that incorporates 3D scalar curvature for 3D image segmentation, inspired by the Einstein-Hilbert functional. To derive its Euler-Lagrange equation, we employ a two-step gradient descent strategy, alternately updating the level set function and its gradient. The paper also establishes the existence and uniqueness of the viscosity solution for the proposed model. Experimental results demonstrate that our proposed model outperforms other state-of-the-art models in 3D image segmentation.

MEDU-Net: channel-based full-resolution contour refinement feedback for patch-aware gland image segmentation

2025-06-14

Yuan Li , Jue D. Wang , Bo Li +1 more | The Journal of Supercomputing

SECOA: Serial Exponential-Crayfish Optimization Algorithm with ResNet features for medical image registration

2025-06-14

P Innasi Lineta , J. Jerusalin Carol | Computational Biology and Chemistry

Weakly-supervised segmentation using sparse single point annotations for lumen and wall of carotid arteries in 3D MRI

2025-06-14

Jonghun Kim , Inye Na , Junmo Kwon +2 more | Computer Methods and Programs in Biomedicine

Annotation-free method for breast tumour segmentation in dynamic contrast-enhanced MRI

2025-06-13

Jiahui He , Junjie Zhang , Xu Huang +7 more | Biomedical Signal Processing and Control

Unsupervised multistage registration method for remote sensing images with large geometric deformation

2025-06-12

Yan Li , Yingdan Wu , Ming Yang +2 more | Journal of Applied Remote Sensing

A new family of compact topological spaces of closed sets with an application to the segmentation problem

2025-06-09

Fernando Sansò | GEM - International Journal on Geomathematics

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation

2025-06-05

Qingxu Meng , Weijiang Wang , Hang Qi +3 more | Scientific Reports

In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare … In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource distribution. Convolutional neural networks (CNNs) perform extremely well in processing local detailed features. However, there restricted receptive field renders them incapable of capturing global context information. Although the combination of CNNs and Transformers balances the ability to capture local detailed features and global context information, it inevitably increases the model's parameters and computational cost, which restricts its equal deployment in real medical scenarios. To address this issue, We propose a Lightweight Network with Roberts edge enhancement (LR-Net) for brain tumor segmentation that achieves an optimal balance between parameters and diagnostic accuracy. We propose a 3D Spatial Shift Convolution and Pixel Shuffle (SSCPS) module, the SSCPS module present a low-parameter, low-computational-cost spatial shift convolution that overcomes the limitation of receptive field and improves the ability to extract global contextual information, Pixel Shuffle (PS) module extracts spatial information from feature dimensions, efficiently replacing traditional upsampling module. The Channel Dilation Mechanism in SSCPS module dynamically adjust the number of output channels to maintain the range and depth of network feature aggregation. Additionally, the network leverages a combination of Channel Attention and Roberts Edge Enhancement (CAREE) module, to improve the channel aggregation capability and sensitivity of fuzzy boundaries. Our method achieved Dice of 0.806, 0.881, and 0.860 in BraTS2019, BraTS2020, and BraTS2021 datasets, while the parameters is only 4.72 M, which is only 3.03% of UNETR's and 28.92% of UNet3D's. This balance of efficiency and accuracy makes the proposed network well-suited for practical clinical applications.

UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction

2025-06-04

Luohong Wu , Nicola Cavalcanti , Matthias Seibold +7 more | Computers in Biology and Medicine

Tensor Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising

2025-06-03

Kaito Hosono , Takamichi Miyata , Hirotsugu Kinoshita