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

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Three-dimensional reconstruction of upper airways from MDCT

Three-dimensional reconstruction of upper airways from MDCT

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Conference on image processing - algorithms and systems IV

作者： Perchet, D Fetita, C Prêteux, F Inst Natl Telecommun ARTEMIS Project Unit Grp Ecoles Telecommun F-91011 Evry France

ISBN: (纸本)0819456454

Under the framework of clinical respiratory investigation, providing accurate modalities for morpho-functional analysis is essential for diagnosis improvement, surgical planning and follow-up. This paper focuses on the upper airways investigation and develops an automated approach for 3D mesh reconstruction from MDCT acquisitions. In order to overcome the difficulties related to the complex morphology of the tipper airways and to the image gray level heterogeneity of the airway lumens and thin bony septa, the proposed 3D reconstruction methodology combines 2D segmentation and 3D surface regularization approaches. The segmentation algorithm relies on mathematical morphology theory and provides airway lumen robust discrimination front the surrounding tissues, while preserving the connectivity relationship between the different. anatomical structures. Tire 3D regularization step uses an energy-based modeling in order to achieve a smooth and well-fitted 3D surface of the upper airways. An accurate 3D mesh representation of the reconstructed airways makes it possible to develop specific clinical applications such as virtual endoscopy, surgical planning anti computer assisted intervention. In addition, building up patient-specific 3D models of upper airways is highly valuable for the study and design of inhaled medication delivery via computational fluid dynamics (CFD) simulation.

关键词： image segmentation 3D modeling 3D image processing Tissues 3D acquisition Binary data Mathematical modeling Mathematical morphology Natural surfaces image processing algorithms and systems

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Lossless/lossy compression of bi-level images 2

Lossless/lossy compression of bi-level images

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Conference on Color Imaging - Device-Independent Color, Color Hard Copy, and Graphic Arts II

作者： Martins, B Forchhammer, S Technical Univ. of Denmark (Denmark)

ISBN: (纸本)0819424293

We present a general method for lossless/lossy coding of bi-level images. The compression and decompression method is analoguous to JBIG, a current international standard for bi-level image compression, and is based on arithmetic coding and a template to determine the coding state. Loss is introduced in a preprocess on the encoding side by flipping pixels in a controlled manner. The method is primarily aimed at halftoned images as a supplement to the specialized soft pattern matching techniques which work better for text. The new algorithm also works well on documents of mixed contents e.g. halftoning and text without any segmentation of the image. The decoding is usually slower than JBIG due to a more wide-spread template. A decoding output of more than 1 Mpixels per second can be obtained in software implementations. We present a greedy 'rate-distortion' algorithm for flipping as well as less complex algorithms intended for relatively fast encoding and moderate latency. In the less complex algorithms, flipping and encoding is carried out in the same pass. The potential risk of flipping avalanches is minimized by conditioning flipping on the sign and magnitude of the local grey-scale error computed by a forgetful error diffusion algorithm. Template based refinement coding is applied for a lossy-to-lossless refinement step. The (de)coding method is proposed as part of JBIG-2, an emerging international standard for lossless/lossy compression of bi-level images.

关键词： image compression Computer programming Distortion Halftones Raster graphics Diffusion image processing algorithms and systems image quality standards Information operations Standards development

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INFRARED image SEGMENTATION THROUGH ITERATIVE THRESHOLDING

INFRARED IMAGE SEGMENTATION THROUGH ITERATIVE THRESHOLDING

引用

2ND CONF ON REAL-TIME image processing

作者： LAVAGETTO, F Univ. di Genova (Italy)

ISBN: (纸本)0819403466

A real time segmentation algorithm for infrared sequences is presented, based on the iterative application of thermal histogram processing and thresholding. The segmentation algorithm is intrinsically not sequential and may therefore be decomposed into a graph of concurrent processing tasks well suited to a parallel implementation. The algorithm is based on the assumption that two pixel populations are present, namely object and background. Since in this hypothesis the separation goodness function should have one single maximum, the algorithm forces this behaviour by redistributing the area of the histogram, i.e. by progressively brightening the target population. An initialization phase finds out potential target areas throughout the current frame in cooperation with a temporal tracking and labeling task, and compiles the search windows set. For each search window the thermal histogram is computed. Then a family of modified histograms is obtained by removing greater and greater areas from the hot tail of the original histogram and by replacing it with an impulse of the same area at the highest extreme of the thermal range. Each of these histograms enters a module which computes a separation goodness function. The separation function presents two adjacent segments: the high thermal segment and the low thermal segment. The former is a constant segment which lasts from the high extreme of the thermal range down to the lowest thermal value of the removed area, the latter has a variable shape and lasts as far as the lowest extreme of the thermal range. The iteration is stopped as soon as the variable segment reaches a monotonically decreasing behaviour. The boundary value between the two segments is chosen as threshold within that window. Experimental results are presented.

关键词： image segmentation image processing Infrared radiation Thermography Infrared imaging Thermal modeling Algorithm development Binary data Detection and tracking algorithms image processing algorithms and systems

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MOTION ESTIMATION IN DIGITAL ANGIOGRAPHIC imageS USING SKELETONS

MOTION ESTIMATION IN DIGITAL ANGIOGRAPHIC IMAGES USING SKELE...

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1990 MIDWEST CONF ON APPLICATIONS OF OPTICAL ENGINEERING : ( OE/MIDWEST 90 )

作者： KWAK, JY EFSTRATIADIS, SN KATSAGGELOS, AK SAHAKIAN, AV SULLIVAN, BJ SWIRYN, S HUETER, DC FROHLICH, T Northwestern Univ. (United States) Northwestern Univ. Medical School (United States)

ISBN: (纸本)0819404772

This paper deals with the estimation of the motion field in digital angiographic sequences. An approach is developed according to which each frame is first segmented into a moving object of interest and the background. The original images are converted into binary images by using a Gaussian smoothing filter and thresholding. In the binary images pixels in moving objects have the value of " 1" and pixels in the background have the value of " 0" . The moving objects in the binary images are thinned to skeletons with unit width by using the Safe-Point Thinning Algorithm (SPTA) with restriction windows we introduce. Then a block-matching algorithm is used in estimating the motion for the pixels which belong to the skeleton. This approach to motion estimation results in reduced computations since only binary multiplications need to be performed for determining the match between two blocks. Therefore an effective searching method is proposed for finding the direction of displacement in successive skeleton frames. Very satisfactory results are obtained by applying the algorithm to 64 x 64. pixel digital angiographic image sequences. 1

关键词： Motion estimation Binary data image processing image segmentation Optical engineering Angiography image processing algorithms and systems Gaussian filters Cardiology Motion models

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BIOMEDICAL STRUCTURE RECOGNITION BY SUCCESSIVE-APPROXIMATIONS

BIOMEDICAL STRUCTURE RECOGNITION BY SUCCESSIVE-APPROXIMATION...

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CONF ON VISUAL COMMUNICATIONS AND image processing : image processing

作者： VENTURI, G DELLEPIANE, S VERNAZZA, G Univ. of Genoa (Italy)

ISBN: (纸本)0819407437

A knowledge-based system is presented that has been designed to overcome the difficulties generally encountered in the extraction of structures from biomedical images. Different segmentation methods are opportunistically applied and parameter values are automatically controlled through the use of models, data, and progressive results. Detected structures are assigned fuzzy membership values related to the reliability of recognition results. The application of the system to microscopic images is described. Peculiar features of the system include a high degree of tolerance to parameter variations high flexibility and a reduced processing time.

关键词： image segmentation image processing Data modeling Biomedical optics Visual communications image processing algorithms and systems Reliability Tomography Detection and tracking algorithms Fuzzy logic

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Planar Surface Detection and Extraction in image Pair Taken under Urban Environment

Planar Surface Detection and Extraction in Image Pair Taken ...

引用

Conference on Signal processing, Sensor Fusion, and Target Recognition XIX

作者： He, Qiang Chu, Chee-Hung Henry Mississippi Valley State Univ Dept Math Comp & Informat Sci Itta Bena MS 38941 USA Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA

ISBN: (纸本)9780819481610

Planar surfaces are important characteristics in man-made environments. Planes have many practical applications in computer vision and computer graphics, including camera calibration and interactive modeling. Here, we develop a plane detection method for piecewise image pair taken under urban environment. All potential planes are detected based on planar homographies estimated from Levenberg-Marquardt algorithm. In order to extract the whole planes, the normalized cut method is used to segment the original images. We pick those segmented regions with maximum fit to those features satisfied with planar homographies as the whole planes. We illustrate the algorithm's performance on gray and color image pairs.

关键词： image segmentation Calibration Cameras Environmental sensing image processing algorithms and systems Computer vision technology Feature extraction Machine vision Buildings Visual process modeling

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MACHINE-PRINTED ARABIC OCR 22

MACHINE-PRINTED ARABIC OCR

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22nd AIPR Workshop on interdisciplinary Computer Vision: Applications and Changing Needs

作者： HASSIBI, K MITEK SYST INC AUTOMAT DOCUMENT RECOGNIT GRPSAN DIEGOCA 92121

ISBN: (纸本)0819413909

This paper presents a brief overview of our research in the development of an OCR system for recognition of machine-printed texts in languages that use the Arabic alphabet. The cursive nature of machine-printed Arabic makes the segmentation of words into letters a challenging problem. In our approach, through a novel preliminary segmentation technique, a word is broken into pieces where each piece may not represent a valid letter in general. Neural networks trained on a training sample set of about 500 Arabic text images are used for recognition of these pieces. The rules governing the alphabet and character-level contextual information are used for recombining these pieces into valid letters. Higher-level contextual analysis schemes including the use of an Arabic lexicon and n-grams is also under development and are expected to improve the word recognition accuracy. The segmentation, recognition, and contextual analysis processes are closely integrated using a feedback scheme. The details of preparation of the training set and some recent results on training of the networks will be presented.

关键词： image segmentation Neural networks Detection and tracking algorithms Optical character recognition image processing algorithms and systems Algorithm development Raster graphics image processing Feedback loops Pattern recognition

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FUZZY OBJECTS AND THEIR BOUNDARIES

FUZZY OBJECTS AND THEIR BOUNDARIES

引用

3rd Conference on Visualization in Biomedical Computing 1994 (VBC 94)

作者： UDUPA, JK SAMARASEKERA, S VENUGOPAL, KP GREVERA, G UNIV PENN DEPT RADIOLPHILADELPHIAPA 19104

ISBN: (纸本)0819416983

Measured data are inherently inaccurate. Operations done on data for defining, visualizing, manipulating and analyzing object information should attempt to retain these inaccuracies as accurately as possible. The theory of fuzzy sets is a proper mathematical vehicle for this purpose. In this attempt, topological notions such as adjacency, connectedness, and boundary need to be developed starting from fuzzy sets. We develop such a framework in this paper and present algorithms for finding fuzzy connected components and boundaries in digital imagery. We demonstrate the usefulness of these algorithms in medical applications, particularly in separating objects that come close together which are otherwise difficult to segment using hard (non-fuzzy) criteria.

关键词： Fuzzy logic Visualization Surgery Visual analytics Algorithm development image segmentation Volume rendering Binary data Biomedical optics image processing algorithms and systems

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Active contour-based multiresolution transforms for the segmentation of fluorescence microscope images

Active contour-based multiresolution transforms for the segm...

引用

Conference on Wavelets XII

作者： Srinivasa, Gowri Fickus, Matthew Kovacevic, Jelena Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Air Force Inst Technol Dept Math & Stat Wright Patterson AFB OH USA

ISBN: (纸本)9780819468499

In recent years, the focus in biological science has shifted to understanding complex systems at the cellular and molecular levels, a task greatly facilitated by fluorescence microscopy. Segmentation, a fundamental yet difficult problem, is often the first processing step following acquisition. We have previously demonstrated that a stochastic active contour based algorithm together with the concept of topology preservation TPSTACS) successfully segments single cells from multicell images. In this paper we demonstrate that TPSTACS successfully segments images from other imaging modalities such as DIC microscopy, MRI and fMRI. While this method is a viable alternative to hand segmentation, it;is not yet ready to be used for high-throughput applications due to its large run time. Thus, we highlight some of the benefits of combining TPSTACS with the multiresolution approach for the segmentation of fluorescence microscope images. Here we propose a multiscale active contour (MSAC) transformation framework for developing a family of modular algorithms for the segmentation of fluorescence microscope images in particular, and biomedical. images in general. While this framework retains the flexibility and the high quality of the segmentation provided by active contour-based algorithms, it offers a boost in the efficiency as well as a framework to compute new features that further enhance the segmentation.

关键词： image segmentation Luminescence Microscopes Proteins image processing algorithms and systems Algorithm development Microscopy Magnetic resonance imaging Transform theory Digital image correlation

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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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