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24
A Generative Model for Image Segmentation Based on Label Fusion
- IEEE TRANSACTIONS IN MEDICAL IMAGING
, 2010
"... We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels ..."
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Cited by 62 (5 self)
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We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation
Tuner: Principled parameter finding for image segmentation algorithms using visual response surface exploration
- IEEE Transactions on Visualization and Computer Graphics
, 2011
"... Abstract—In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage techn ..."
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Cited by 22 (6 self)
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Abstract—In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the ”goodness ” of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans. Index Terms—Parameter exploration, Image segmentation, Gaussian Process Model. 1
Goldberg I: Computer vision for microscopy applications
- In Obinata G, Dutta A (eds): Vision Systems: Segmentation and Pattern Recognition
"... The tremendous growth in digital imagery has introduced the need for accurate image ..."
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Cited by 12 (5 self)
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The tremendous growth in digital imagery has introduced the need for accurate image
Data clustering as an optimum-path forest problem with applications in image analysis
- INTERN. JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (IJIST
, 2009
"... We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a connectivity function is maximized, such that each maximum of t ..."
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Cited by 8 (0 self)
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We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a connectivity function is maximized, such that each maximum of the pdf becomes root of an optimum-path tree (cluster), composed by samples "more strongly connected" to that maximum than to any other root. We discuss the advantages over other pdf-based approaches and present extensions to large datasets with results for interactive image segmentation and for fast, accurate, and automatic brain tissue classification in magnetic resonance (MR) images. We also include experimental comparisons with other clustering approaches. VC 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 50–68, 2009; Published online in Wiley
Brain MRI Tissue Classification Based on Local Markov Random Fields
"... A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same c ..."
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Cited by 3 (1 self)
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A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous Markov Random Field and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume
A modified adaptive fuzzy c-means clustering algorithm for brain MR image segmentation
- International Journal of Engineering Research & Technology (IJERT
, 2012
"... Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accu ..."
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Cited by 2 (0 self)
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Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, a modified adaptive fuzzy c-means clustering (AFCM) algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The proposed method has been successfully applied to recorded MR images with desirable results. Our results show that the proposed AFCM algorithm can effectively segment the test images and MR images. Comparisons with other FCM approaches based on number of iterations and time complexity demonstrate the superior performance of the proposed algorithm. 1.
Gender differences in cerebral cortical folding: multivariate complexity-shape analysis with insights into handling brain-volume differences
- Proc. Int. Conf. Medical Image Computing and Computer Assisted Intervention
"... Abstract. This paper presents a study of gender differences in adult human cerebral cortical folding patterns. The study employs a new mul-tivariate statistical descriptor for analyzing folding patterns in a region of interest (ROI) and a rigorous nonparametric permutation-based scheme for hypothesi ..."
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Abstract. This paper presents a study of gender differences in adult human cerebral cortical folding patterns. The study employs a new mul-tivariate statistical descriptor for analyzing folding patterns in a region of interest (ROI) and a rigorous nonparametric permutation-based scheme for hypothesis testing. Unlike typical ROI-based methods that summa-rize folding complexity or shape by single/few numbers, the proposed de-scriptor systematically constructs a unified description of complexity and shape in a high-dimensional space (thousands of numbers/dimensions). Furthermore, this paper presents new mathematical insights into the re-lationship of intra-cranial volume (ICV) with cortical complexity and shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to “com-plexity”. This paper describes two systematic methods for handling ICV changes in folding studies using the proposed descriptor. The clinical study in this paper exploits these theoretical insights to demonstrate that (i) the answer to which gender has higher/lower “complexity ” de-pends on how a folding measure handles ICV differences and (ii) cortical folds in males and females differ significantly in shape as well. 1
Segmentation of Brain Magnetic Resonance Images (MRIs): A Review
"... Abstract MR imaging modality has assumed an important position in studying the characteristics of soft tissues. Generally, images acquired by using this modality are found to be affected by noise, partial volume effect (PVE) and intensity nonuniformity (INU). The presence of these factors degrades ..."
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Abstract MR imaging modality has assumed an important position in studying the characteristics of soft tissues. Generally, images acquired by using this modality are found to be affected by noise, partial volume effect (PVE) and intensity nonuniformity (INU). The presence of these factors degrades the quality of the image. As a result of which, it becomes hard to precisely distinguish between different neighboring regions constituting an image. To address this problem, various methods have been proposed. To study the nature of various proposed state-of-the-art medical image segmentation methods, a review was carried out. This paper presents a brief summary of this review and attempts to analyze the strength and weaknesses of the proposed methods. The review concludes that unfortunately, none of the proposed methods has been able to independently address the problem of precise segmentation in its entirety. The paper strongly favors the use of some module for restoring pixel intensity value along with a segmentation method to produce efficient results.
