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检索条件"主题词=Image processing algorithms and systems"

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Registration of Segmented Histological images using Thin Plate Splines and Belief Propagation

Registration of Segmented Histological Images using Thin Pla...

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Conference on Medical Imaging - image processing

作者： Kybic, Jan Czech Tech Univ CR-16635 Prague Czech Republic

ISBN: (纸本)9780819498274

We register images based on their multiclass segmentations, for cases when correspondence of local features cannot be established. A discrete mutual information is used as a similarity criterion. It is evaluated at a sparse set of location on the interfaces between classes. A thin-plate spline regularization is approximated by pairwise interactions. The problem is cast into a discrete setting and solved efficiently by belief propagation. Further speedup and robustness is provided by a multiresolution framework. Preliminary experiments suggest that our method can provide similar registration quality to standard methods at a fraction of the computational cost.

关键词： image segmentation image registration Kidney Binary data Current controlled current source image processing image processing algorithms and systems Medical imaging Optimization (mathematics) Tissues

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Multi-Atlas Propagation via a Manifold Graph on a Database of Both Labeled and Unlabeled images

Multi-Atlas Propagation via a Manifold Graph on a Database o...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Gao, Qinquan Tong, Tong Rueckert, Daniel Edwards, P. J. Eddie Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Imperial Coll London Dept Surg & Cancer London England

ISBN: (纸本)9780819498281

We present a framework for multi-atlas based segmentation in situations where we have a small number of segmented atlas images, but a large database of unlabeled images is also available. The novelty lies in the application of graph-based registration on a manifold to the problem of multi-atlas registration. The approach is to place all the images in a learned manifold space and construct a graph connecting near neighbors. Atlases are selected for any new image to be segmented based on the shortest path length along the manifold graph. A multi-scale non-rigid registration takes place via each of the nodes on the graph. The expectation is that by registering via similar images, the likelihood of misregistrations is reduced. Having registered multiple atlases via the graph, patch-based voxel weighted voting takes place to provide the final segmentation. We apply this approach to a set of T2 MRI images of the prostate, which is a notoriously difficult segmentation task. On a set of 25 atlas images and 85 images overall, we see that registration via the manifold graph improves the Dice coefficient from 0.82 +/- 0.05 to 0.86 +/- 0.03 and the average symmetrical boundary distance from 2.89 +/- 0.62 mm to 2.47 +/- 0.51 mm. This is a modest but potentially useful improvement in a difficult set of images. It is expected that our approach will provide similar improvement to any multi-atlas segmentation task where a large number of unsegmented images are available.

关键词： image segmentation image registration Prostate Databases Magnetic resonance imaging image fusion image processing algorithms and systems Cancer Electroluminescent displays Neuroimaging

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Improving whole-brain segmentations through incorporating regional image intensity statistics

Improving whole-brain segmentations through incorporating re...

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Conference on Medical Imaging - image processing

作者： Ledig, Christian Heckemann, Rolf A. Hammers, Alexander Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Dept Comp Biomed Image Anal Grp London SW7 2AZ England

ISBN: (纸本)9780819494436

Multi-atlas segmentation methods are among the most accurate approaches for the automatic labeling of magnetic resonance (MR) brain images. The individual segmentations obtained through multi-atlas propagation can be combined using an unweighted or locally weighted fusion strategy. Label overlaps can be further improved by refining the label sets based on the image intensities using the Expectation-Maximisation (EM) algorithm. A drawback of these approaches is that they do not consider knowledge about the statistical intensity characteristics of a certain anatomical structure, especially its intensity variance. In this work we employ learned characteristics of the intensity distribution in various brain regions to improve on multi-atlas segmentations. Based on the intensity profile within labels in a training set, we estimate a normalized variance error for each structure. The boundaries of a segmented region are then adjusted until its intensity characteristics are corrected for this variance error observed in the training sample. Specifically, we start with a high-probability "core" segmentation of a structure, and maximise the similarity with the expected intensity variance by enlarging it. We applied the method to 35 datasets of the OASIS database for which manual segmentations into 138 regions are available. We assess the resulting segmentations by comparison with this gold-standard, using overlap metrics. Intensity-based statistical correction improved similarity indices (SI) compared with EM-refined multi-atlas propagation from 75.6% to 76.2% on average. We apply our novel correction approach to segmentations obtained through either a locally weighted fusion strategy or an EM-based method and show significantly increased similarity indices.

关键词： image segmentation Brain Expectation maximization algorithms image fusion Error analysis Neuroimaging Americium Magnetic resonance imaging image processing algorithms and systems Magnetism

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Fuzzy model based object delineation via energy minimization

Fuzzy model based object delineation via energy minimization

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Conference on Medical Imaging - image processing

作者： Ciesielski, Krzysztof Chris Udupa, Jayaram K. Odhner, Dewey Zhao, Limimg W Virginia Univ Dept Math Morgantown WV 26506 USA

ISBN: (纸本)9780819494436

We study the problem of automatic delineation of an anatomic object in an image, where the object is solely identified by its anatomic prior. We form such priors in the form of fuzzy models to facilitate the segmentation of images acquired via different imaging modalities (like CT, MRI, or PET), in which the recorded image properties are usually different. Our main interest is in delineating different body organs in medical images for automatic anatomy recognition (AAR). The AAR system we are developing consists of three main components: (Cl) building body-wide groupwise fuzzy anatomic models;(C2) recognizing the body organs geographically and then delineating them by employing the models;(C3) generating quantitative descriptions. This paper focuses on (C2) and presents a unified approach for model-based segmentation within which several different strategies can be formulated, ranging from model-based hard/fuzzy thresholding to model-based graph cut, fuzzy connectedness, and random walker methods and algorithms. This is an important theoretical advance. The presented experiments clearly prove, that a fully automatic segmentation system based on the fuzzy models can indeed provide the reliable segmentations. However, the presented experiments utilize only the simplest versions of the methodology presented in the theoretical part of the paper. The full experimental evaluation of the methodology is still a work in progress.

关键词： image segmentation Fuzzy logic Detection and tracking algorithms image processing algorithms and systems Neodymium Digital imaging Remote sensing Medical imaging Computed tomography Magnetic resonance imaging

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Encoding Scene Structures for Video Compression

Encoding Scene Structures for Video Compression

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Conference on Applications of Digital image processing XXXV

作者： Georgiadis, Georgios Ravichandran, Avinash Soatto, Stefano Chiuso, Alessandro Univ Calif Los Angeles UCLA Vis Lab Los Angeles CA 90095 USA Univ Padua Dept Informat Engn I-35131 Padua Italy

ISBN: (纸本)9780819492166

We describe an approach to partition a video stream into structure regions that are temporally encoded and disjoint from texture regions, that are synthesized so as to preserve the statistical properties of the original data stream. Structures encode regions of an image that can be put into correspondence in different images of the same scene, and are encoded via a dictionary that takes into account spatial and temporal regularities. Textures are synthesized in a manner that preserves perceptual similarity.

关键词： image segmentation Video compression Associative arrays Sensors Computer programming Video image compression Algorithm development image processing image processing algorithms and systems

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A single-imager, single-lens video camera prototype for 3D imaging

A single-imager, single-lens video camera prototype for 3D i...

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Conference on Three-Dimensional image processing (3DIP) and Applications II

作者： Christopher, Lauren A. Li, Weixu Indiana Univ Purdue Univ Indianapolis IN 46202 USA

ISBN: (纸本)9780819489371

A new method for capturing 3D video from a single imager and lens is introduced. The benefit of this method is that it does not have the calibration and alignment issues associated with binocular 3D video cameras. It also does not require special ranging transmitters and sensors. Because it is a single lens/imager system, it is also less expensive than either the binocular or ranging cameras. Our system outputs a 2D image and associated depth image using the combination of microfluidic lens and Depth from Defocus (DfD) algorithm. The lens is capable of changing the focus to obtain two images at the normal video frame rate. The Depth from Defocus algorithm uses the in focus and out of focus images to infer depth. We performed our experiments on synthetic and on the real aperture CMOS imager with a microfluidic lens. On synthetic images, we found an improvement in mean squared error compared to the literature on a limited test set. On camera images, our research showed that DfD combined with edge detection and segmentation provided subjective improvements in the resulting depth images.

关键词： Cameras Imaging systems Video image segmentation Microfluidics image processing Prototyping Digital filtering Stereoscopic cameras image processing algorithms and systems

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Multi-level feature extraction for skin lesion segmentation in dermoscopic images

Multi-level feature extraction for skin lesion segmentation ...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Khakabi, Sina Wighton, Paul Lee, Tim K. Atkins, M. Stella Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780819489647

This paper presents a novel approach in computer aided skin lesion segmentation of dermoscopic images. We apply spatial and color features in order to model the lesion growth pattern. The decomposition is done by repeatedly clustering pixels into dark and light sub-clusters. A novel tree structure based representation of the lesion growth pattern is constructed by matching every pixel sub-cluster with a node in the tree structure. This model provides a powerful framework to extract features and to train models for lesion segmentation. The model employed allows features to be extracted at multiple layers of the tree structure, enabling a more descriptive feature set. Additionally,there is no need for preprocessing such as color calibration or artifact disocclusion. Preliminary features (mean over RGB color channels) are extracted for every pixel over four layers of the growth pattern model and are used in association with radial distance as a spatial feature to segment the lesion. The resulting per pixel feature vectors of length 13 are used in a supervised learning model for estimating parameters and segmenting the lesion. A dataset containing 116 challenging images from dermoscopic atlases is used to validate the method via a 10-fold cross validation procedure. Results of segmentation are compared with six other skin lesion segmentation methods. Our method outperforms five other methods and performs competitively with another method. We achieve a per-pixel sensitivity/specificity of 0.890 and 0.901 respectivey.

关键词： image segmentation Skin Feature extraction RGB color model image processing algorithms and systems Machine learning Melanoma Calibration Cancer Microscopy

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A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them

A unifying graph-cut image segmentation framework: algorithm...

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Conference on Medical Imaging - image processing

作者： Ciesielski, Krzysztof Chris Udupa, Jayaram K. Falcao, A. X. Miranda, P. A. V. W Virginia Univ Dept Math Morgantown WV 26506 USA Univ Penn MIPG Dept Radiol Philadelphia PA 19104 USA Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Sao Paulo IME Dept Comp Sci Sao Paulo Brazil

ISBN: (纸本)9780819489630

We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the l(p) norm, 1 <= p <= infinity. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC). The norm-based description of GGC provides more elegant and mathematically better recognized framework of our earlier results from [18, 19]. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper [22] (min-cut/max-flow graph cut, random walker, shortest path/geodesic, Voronoi diagram, power watershed/shortest path forest), which optimize, via l(p) norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used l(p) energy function. Actually, the comparison of the GGC representable algorithms with that encompassed in the framework described in [22] constitutes the main contribution of this work.

关键词： Fuzzy logic Detection and tracking algorithms Evolutionary algorithms image segmentation algorithms image processing algorithms and systems Artificial intelligence Optimization (mathematics) Digital imaging Surface plasmons

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Edge-Preserving Metal Artifact Reduction

Edge-Preserving Metal Artifact Reduction

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Meyer, Esther Raupach, Rainer Lell, Michael Schmidt, Bernhard Kachelriess, Marc Univ Erlangen Nurnberg Inst Med Phys D-91054 Erlangen Germany Siemens Hlthcare Forchheim Forchheim Germany Univ Erlangen Nurnberg Inst Diagnost Radiol Erlangen Germany DKFZ Heidelberg Germany

ISBN: (纸本)9780819489623

Metal implants in the field of measurement lead to strong artifacts in CT images and reduce the image quality and the diagnostic value severely. We introduce frequency split metal artifact reduction (FSMAR), a conceptually new MAR method which is designed to reduce metal artifacts and preserve details and edges of structures even close to metal implants. There are many MAR methods which simply replace unreliable parts of the projection data by inpainting. FSMAR is a combination of an inpainting-based MAR method with a frequency split approach. Normalized metal artifact reduction (NMAR) is chosen as the inpainting-based MAR method in this work. The high frequencies of the original image, where all rawdata were used for the reconstruction, are combined with an NMAR-corrected image. NMAR uses a normalization step to reduce metal artifacts without introducing severe new artifacts. algorithms using a frequency split were already used in CT for example to reduce cone-beam artifacts. FSMAR is tested for patient datasets with different metal implants. The study includes patients with hip prostheses, a neuro coil, and a spine fixation. All datasets were scanned with modern clinical dual source CT scanners. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to metal implants.

关键词： Metals Computed tomography image segmentation Spine Bone image filtering image quality Diagnostics Linear filtering image processing algorithms and systems

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Automated Localization of Vertebra Landmarks in MRI images

Automated Localization of Vertebra Landmarks in MRI Images

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Conference on Medical Imaging 2011 - Computer-Aided Diagnosis

作者： Pai, Akshay Narasimhamurthy, Anand Rao, V. S. Veeravasarapu Vaidya, Vivek GE Global Res Bangalore Karnataka India

ISBN: (纸本)9780819485052

The identification of key landmark points in an MR spine image is an important step for tasks such as vertebra counting. In this paper, we propose a template matching based approach for automatic detection of two key landmark points, namely the second cervical vertebra (C2) and the sacrum from sagittal MR images. The approach is comprised of an approximate localization of vertebral column followed by matching with appropriate templates in order to detect/localize the landmarks. A straightforward extension of the work described here is an automated classification of spine section(s). It also serves as a useful building block for further automatic processing such as extraction of regions of interest for subsequent image processing and also in aiding the counting of vertebra.

关键词： Magnetic resonance imaging Spine image processing image segmentation Pathology Computer aided diagnosis and therapy Current controlled current source image processing algorithms and systems image quality Medical imaging

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