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Multi-classifier framework for atlas-based image segmentation

PATTERN RECOGNITION LETTERS 2005年第13期26卷 2070-2079页

作者： Rohlfing, T Maurer, CR Stanford Univ Dept Neurosurg Stanford CA 94305 USA

Three different systematic approaches to generate multiple classifiers in atlas-based biomedical image segmentation are compared. Different atlases, as well as different parametrization of the registration algorithm, lead to different atlas-based classifiers. The classifier outputs are combined and compared to a manual ground truth segmentation. Classifier combination consistently improved classification accuracy with the biggest improvement from multiple atlases. We conclude that multi-classifier techniques have a natural application to atlas-based segmentation and increase classification accuracy in real-world segmentation problems. (c) 2005 Elsevier B.V. All rights reserved.

关键词： atlas-based segmentation multiple classifier system bagging non-rigid registration

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REFERENCE-based PROBABILISTIC segmentation AS NON-RIGID REGISTRATION USING THIN PLATE SPLINES

REFERENCE-BASED PROBABILISTIC SEGMENTATION AS NON-RIGID REGI...

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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Bertelli, Luca Ghosh, Pratim Manjunath, B. S. Gibou, Frederic Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Univ Calif Santa Barbara Dept Mech Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781424417650

In this paper we demonstrate the effectiveness of reference (or atlas)-based non-rigid registration for the segmentation of medical and biological imagery. In particular we introduce a segmentation functional exploiting feature information about the reference image and we minimize it with respect to the parameters of the non-rigid transformation, akin to a region-based maximum likelihood estimation process. The warping transformation is modeled using Thin Plate Splines, which incorporate information about the global rigid motion and the non-rigid local displacements. Extensive experimental evaluations and comparisons with other segmentation techniques on a complex biological dataset are presented. The proposed algorithm outperforms the others in both classification rate and, in particular, localization accuracy.

关键词： atlas-based segmentation region based segmentation non-rigid registration

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atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2007年第1期54卷 122-129页

作者： Zhou, Yongxin Bai, Jing Tsing Hua Univ Dept Biomed Engn Beijing 100084 Peoples R China

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

关键词： atlas-based segmentation bias field correction brain MRI fuzzy connectedness

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Multiple abdominal organ segmentation: An atlas-based fuzzy connectedness approach

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2007年第3期11卷 348-352页

作者： Zhou, Yongxin Bai, Jing Tsing Hua Univ Dept Biomed Engn Beijing 100084 Peoples R China

Organ segmentation is an important step in various medical image applications. In this paper, a presegmented atlas is incorporated into the fuzzy connectedness (FC) framework for automatic segmentation of abdominal organs. First, the atlas is registered onto the subject to provide an initial segmentation. Then, a novel method is applied to estimate the necessary FC parameters such as organ intensity features, seeds, and optimal FC threshold automatically and subject adaptively. In order to overcome the intensity overlapping between the neighboring organs, a shape modification approach based on Euclidean distance and watershed segmentation is used. This atlas-based segmentation method has been tested on some abdominal CT and MRI images from Chinese patients. Experimental results indicate the validity of this segmentation method for various image modalities.

关键词： abdominal organs atlas-based segmentation fuzzy connectedness (FC)

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Parallel optimization of tumor model parameters for fast registration of brain tumor images

Parallel optimization of tumor model parameters for fast reg...

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Medical Imaging 2008 Conference

作者： Zacharaki, Evangelia I. Hogea, Cosmina S. Shen, Dinggang Biros, George Davatzikos, Christos Univ Penn Sect Biomed Image Anal Philadelphia PA 19104 USA Univ Penn Dept Mech Engn & Appl Mech Philadelphia PA 19104 USA

ISBN: (纸本)9780819470980

The motivation of this work is to register MR brain tumor images with a brain atlas. Such a registration method can make possible the pooling of data from different brain tumor patients into a common stereotaxic space, thereby enabling the construction of statistical brain tumor atlases. Moreover, it allows the mapping of neuroanatomical brain atlases into the patient's space, for segmenting brains and thus facilitating surgical or radiotherapy treatment planning. However, the methods developed for registration of normal brain images are not directly applicable to the registration of a normal atlas with a tumor-bearing image, due to substantial dissimilarity and lack of equivalent image content between the two images, as well as severe deformation or shift of anatomical structures around the tumor. Accordingly, a model that can simulate brain tissue death and deformation induced by the tumor is considered to facilitate the registration. Such tumor growth simulation models are usually initialized by placing a small seed in the normal atlas. The shape, size and location of the initial seed are critical for achieving topological equivalence between the atlas and patient's images. In this study, we focus on the automatic estimation of these parameters, pertaining to tumor simulation. In particular, we propose an objective function reflecting feature-based similarity and elastic stretching energy and optimize it with APPSPACK (Asynchronous Parallel Pattern Search), for achieving significant reduction of the computational cost. The results indicate that the registration accuracy is high in areas around the tumor, as well as in the healthy portion of the brain.

关键词： brain tumor deformable registration atlas-based segmentation tumor simulation APPSPACK

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Automatic segmentation of the facial nerve and chorda tympani using image registration and statistical priors

Automatic segmentation of the facial nerve and chorda tympan...

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Medical Imaging 2008 Conference

作者： Noble, Jack H. Warren, Frank M. Labadie, Robert F. Dawant, Benoit M. Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Univ Utah Div Otolaryngol Salt Lake City UT 84132 USA Vanderbilt Univ Ctr Med Dept Otorhinolaryngol Nashville TN 37232 USA

ISBN: (纸本)9780819470980

In cochlear implant surgery, an electrode array is permanently implanted in the cochlea to stimulate the auditory nerve and allow deaf people to hear. A minimally invasive surgical technique has recently been proposed-percutaneous cochlear access-in which a single hole is drilled from the skull surface to the cochlea. For the method to be feasible, a safe and effective drilling trajectory must be deter-mined using a pre-operative CT. segmentation of the structures of the ear would improve trajectory planning safety and efficiency and enable the possibility of automated planning. Two important structures of the ear, the facial nerve and chorda tympani, present difficulties in intensity based segmentation due to their diameter (as small as 1.0 and 0.4 mm) and adjacent inter-patient variable structures of similar intensity in CT imagery. A multipart, model-based segmentation algorithm is presented in this paper that accomplishes automatic segmentation of the facial nerve and chorda tympani. segmentation results are presented for 14 test ears and are compared to manually segmented surfaces. The results show that mean error in structure wall localization is 0.2 and 0.3 mm for the facial nerve and chorda, proving the method we propose is robust and accurate.

关键词： atlas-based segmentation optimal path model-based segmentation cochlear implant facial nerve chorda tympani

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Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocol microscopy images of bee brains

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NEUROIMAGE 2004年第4期21卷 1428-1442页

作者： Rohlfing, T Brandt, R Menzel, R Maurer, CR Stanford Univ Dept Neurosurg Image Guidance Labs Stanford CA 94305 USA Free Univ Berlin Inst Neurobiol Berlin Germany Indeed Visual Concepts GMBH Berlin Germany

This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. segmentation by intensity-based nonrigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average-shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by the similarity index (SI) between the automatic segmentation result and a manually generated gold standard. The best segmentation accuracy was achieved using the MUL paradigm, which resulted in a mean similarity index value between manual and automatic segmentation of 0.86 (AVG, 0.84;SIM, 0.82;IND, 0.81). The superiority of the MUL strategy over the other three methods is statistically significant (two-sided paired t test, P < 0.001). Both the MUL and AVG strategies performed better than the best possible SIM and IND strategies with optimal a posteriori atlas selection (mean similarity index for optimal SIM, 0.83;for optimal IND, 0.81). Our findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy. (C) 2004 Elsevier Inc. All rights reserved.

关键词： atlas-based segmentation atlas selection nonrigid image registration bee brain confocal microscopy imaging

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

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2004年第8期23卷 983-994页

作者： Rohlfing, T Russakoff, DB Maurer, CR Stanford Univ Dept Neurosurg Image Guidance Labs Stanford CA 94305 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

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

关键词： atlas-based segmentation classifier performance expectation-maximization (EM) parameter estimation mixture of experts multiclassifier decision fusion

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

Performance-based classifier combination in atlas-based imag...

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Meeting on Information Processing in Medical Imaging (IPMI)

作者： Rohlfing, T Russakoff, DB Maurer, CR Stanford Univ Dept Neurosurg Image Guidance Labs Stanford CA 94305 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

关键词： atlas-based segmentation classifier performance expectation-maximization (EM) parameter estimation mixture of experts multiclassifier decision fusion

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atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography

Atlas-based method for segmentation of cerebral vascular tre...

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Medical Imaging 2004 Conference

作者： Passat, N Ronse, C Baruthio, J Armspach, JP Maillot, C Jahn, C Univ Strasbourg CNRS UMR 7005 LSIIT Strasbourg France

ISBN: (纸本)0819452831

Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures. However, it also provides magnitude images, containing anatomical - but no vascular - data. Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images. We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images. This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection. That information can then be transposed from a normal case to any patient image by image registration. An atlas of the whole head has been developed in order to store such anatomical knowledge. It divides a magnitude image into several "vascular areas", each one having specific vessel properties. The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image. The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood. The topology conservation is guaranteed by only selecting simple points during the growing process. The method has been performed on 15 PC-MRA's of the brain. The results have been validated using MIP and 3D surface rendering visualization;a comparison to other results obtained without an atlas proves that atlas-based methods are an effective way to optimize vascular segmentation strategies.

关键词： atlas-based segmentation adaptative region growing simple points magnetic resonance angiography cerebral vascular tree

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