Multi-agent segmentation of IVUS images

Bovenkamp, Ernst G. P.; Dijkstra, Jouke; Bosch, Johan G.; Reiber, Johan H. C.

doi:10.1016/j.patcog.2003.09.015

Cited by 75 publications

(51 citation statements)

References 24 publications

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“…In [6], a contour is propagated along the radial direction with a balloon force. Others utilized active contours through a neighborhood search [7], [8], active contours with a statistical distribution of blood and vessel wall tissue [9], [10], and a knowledge-based approach that incorporates shadow, calcified plaque, and side branch information in a high-level system [11].…”

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confidence: 99%

Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images

Ünal

Bucher

Carlier

et al. 2008

IEEE Trans. Inform. Technol. Biomed.

130

113

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Abstract-Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-densitybased image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.Index Terms-Arterial wall segmentation, calcification detection, intensity prior, intravascular ultrasound (IVUS), lumen segmentation, media adventitia segmentation, model-based segmentation, side branch detection, shape prior.

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mentioning

confidence: 99%

Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images

Ünal

Bucher

Carlier

et al. 2008

IEEE Trans. Inform. Technol. Biomed.

130

113

View full text Add to dashboard Cite

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“…The most applications can be found in the ield of image coregistration and image fusion [24][25][26]. In the ield of object detection and delineation reported applications are still rare [27][28][29][30][31]-even more in the ield of remote sensing image analysis [32][33][34][35].…”

Section: Agent-based Image Analysismentioning

confidence: 99%

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Hofmann¹

2017

Modern Fuzzy Control Systems and Its Applications

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Recent methods of image analysis in remote sensing lack a suicient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal efects, diferent locations, atmospheric conditions, or even a slightly diferent sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents' behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm.Keywords: agent-based image analysis, fuzzy believe-desire-intention model, object-based image analysis, fuzzy control system, remote sensing IntroductionAnalyzing remote sensing data is strongly bound to methods of image processing and image analysis. In contrast to other imaging techniques, remote sensing as per deinition is a method to acquire information about the earth's surface by detecting and analyzing its relected or emited electromagnetic radiation and without being in direct contact with it. Besides radiation © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.of the visual spectrum also infrared (optical data) and microwave radiation (RADAR) is used to produce remote sensing images. The remote sensing instruments can be carried by space crafts (usually satellites) or airborne vehicles (airplanes, drones, etc.). In order to gather geo-information from remote sensing data, the produced images need to be analyzed, that is, preprocessed and classiied. In this context, image classiication means to assign pixels to meaningful object classes of the earth's surface, whereas the delineated and classiied objects are inally stored in a geographic information system (GIS) as polygons, lines, or points ( vector model). With the continuous increase of remote sensing images' spatial (and radiometric) resolution, image analysis in remote sensing became more and more complex. Until...

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“…The goal of these efforts are improving precision and robustness or reducing the processing time. Recent studies in robustness direction have generally focused on providing the agents with knowledge and deliberative capabilities [12,13]. In these works, low-level processing is still carried out by traditional algorithms.…”

Section: Related Workmentioning

confidence: 99%