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10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)

作者： Xu, Yusheng Sun, Zhenghao Hoegner, Ludwig Stilla, Uwe Yao, Wei Tech Univ Munich Photogrammetry & Remote Sensing Munich Germany Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hung Hom Hong Kong Peoples R China

ISBN: (纸本)9781538684795

In this paper, an instance segmentation method for tree extraction from MLS data sets in urban scenes is developed. The proposed method utilizes a supervoxel structure to organize the point clouds, and then extracts the detrended geometric features from the local context of supervoxels. Combined with the detrended features of the local context, the Random Forest (RF) classifier will be adopted to obtain the initial semantic labeling results of trees from point clouds. Afterwards, a local context-based regularization is iteratively performed to achieve global optimum on a global graphical model, in order to spatially smoothing the semantic labeling results. Finally, a graph-based segmentation is conducted to separate individual trees according to the semantic labeling results. The use of supervoxel structure can preserve the geometric boundaries of objects in the scene, and compared with point-based solutions, the supervoxel-based method can largely decrease the number of basic elements during the processing. Besides, the introduction of supervoxel contexts can extract the local information of an object making the feature extraction more robust and representative. Detrended geometric features can get over the redundant and in-salient information in the local context, so that discriminative features are obtained. Benefiting from the regularization process, the spatial smoothing is obtained based on initial labeling results from classic classifications such as RF classification. As a result, misclassification errors are removed to a large degree and semantic labeling results are thus smoothed. based on the constructed global graphical model during the spatially smoothing process, a graph-based segmentation is applied to partition the graphical model for the clustering the instances of trees. The experiments on two test datasets have shown promising results, with an accuracy of the semantic labeling of trees reaching around 0.9. The segmentation of trees using graph-bas

关键词： Instance segmentation MLS trees urban areas supervoxels local context graph-based segmentation

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graph-based RGB-D IMAGE segmentation USING COLOR-DIRECTIONAL-REGION MERGING 44

GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pan, Xiong Zhang, Zejun Liu, Yizhang Yang, Changcai Chen, Qiufeng Cheng, Li Lin, Jiaxiang Chen, Riqing Fujian Agr & Forestry Univ Coll Comp & Informat Sci Digital Fujian Inst Big Data Agr & Forestry Fuzhou Fujian Peoples R China Fujian Agr & Forestry Univ Jinshan Colledge Fuzhou Fujian Peoples R China

ISBN: (纸本)9781479981311

Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging. We evaluate our method on the NYU-v2 depth database and compare it with several published RGB-D partition methods. The experimental results show that our method is comparable with the state-of-the-art methods and provides more details of structures in the scene.

关键词： RGB-D image segmentation graph-based segmentation data-driven weight color-normal-region merging

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graph-based region growing for mass segmentation in digital mammography

Graph-based region growing for mass segmentation in digital ...

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Medical Imaging 2002 Conference

作者： Chu, Y Li, LH Clark, RA Univ S Florida H Lee Moffit Canc Ctr & Res Inst Dept Comp Sci & Engn Tampa FL 33612 USA

ISBN: (纸本)0819444294

Mass segmentation is a vital step in CAD mass detection and classification. A challenge for mass segmentation in mammograms is that masses may contact with some surrounding tissues, which hate the similar intensity. In this paper, a novel graph-based algorithm has been proposed to segment masses in mammograms. In the proposed algorithm, the procedure of region growing is represented as a growing tree whose root is the selected seed. Active leaves, which have the ability to grow, in the connection area between adjacent regions are deleted to stop growing, then separating the adjacent regions while keeping the spiculation of masses, which is a primary sign of malignancy for masses. The new constrained segmentation was tested with 20 cases in USF moffitt mammography database against the conventional region growing algorithm. The segmented mass regions were evaluated in terms of the overlap area with annotations made by the radiologist. We found that the new graph-based segmentation more closely match radiologists' outlines of these masses.

关键词： digital mammography mass segmentation region growing graph-based segmentation

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Fuzzy image segmentation based upon hierarchical clustering

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KNOWLEDGE-based SYSTEMS 2015年第Oct..期87卷 26-37页

作者： Gomez, Daniel Yanez, Javier Guada, Carely Tinguaro Rodriguez, J. Montero, Javier Zarrazola, Edwin Univ Complutense Madrid Fac Estudios Estadist Ave Puerta de Hierro S-N E-28040 Madrid Spain Univ Complutense Madrid Fac Matemat E-28040 Madrid Spain Univ Complutense Madrid Fac Matemat CSIC Inst IGEOUCM E-28040 Madrid Spain Univ Antioquia Inst Matemat Medellin Colombia Univ Complutense Madrid Escuela Estadist Madrid 28040 Spain

In this paper we introduce the concept of Fuzzy Image segmentation, providing an algorithm to build fuzzy boundaries based on the existing relations between the fuzzy boundary set problem and the (crisp) hierarchical image segmentation problem. In particular, since a crisp image segmentation can be characterized in terms of the set of edges that separates the adjacent regions of the segmentation, from these edges we introduce the concept of fuzzy image segmentation. Hence, each fuzzy image segmentation is characterized by means of a fuzzy set over the set of edges, which can be then understood as the fuzzy boundary of the image. Some computational experiences are included in order to show the obtained fuzzy boundaries of some digital images. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Fuzzy set Image segmentation Hierarchical segmentation graph-based segmentation

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SEMI-AUTOMATED segmentation AND QUANTIFICATION OF MITRAL ANNULUS AND LEAFLETS FROM TRANSESOPHAGEAL 3-D ECHOCARDIOgraphIC IMAGES

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ULTRASOUND IN MEDICINE AND BIOLOGY 2015年第1期41卷 251-267页

作者： Sotaquira, Miguel Pepi, Mauro Fusini, Laura Maffessanti, Francesco Lang, Roberto M. Caiani, Enrico G. Politecn Milan Dipartimento Elettron Informat & Bioingn I-20133 Milan Italy IRCCS Ctr Cardiol Monzino Milan Italy Univ Chicago Noninvas Cardiac Imaging Lab Chicago IL 60637 USA

Quantification of three-dimensional (3-D) morphology of the mitral valve (MV) using real-time 3-D-transesophageal echocardiography (RT3-D TEE) has proved to be a valuable tool for the assessment of MV pathologies, but of limited use in clinical practice because it relies on user-intensive approaches. This study presents a new algorithm for the segmentation and morphologic quantification of the mitral annulus (MA) and mitral leaflets (ML) in closed valve configuration from RT3-D TEE volumes. Following initialization, the MA and the ML and the coaptation line (CL) are automatically obtained in 3-D. Validation with manual tracings was performed on 33 patients, resulting in segmentation errors in the order of 0.7 mm and 0.6 mm for the MA and ML segmentation, in addition to good intra-and inter-observer reproducibility (coefficients of variation below 12% and 15%, respectively). The ability of the algorithm to assess different MV pathologies as well as repaired valves with implanted annular rings was also explored. The reported performance of the proposed fast, semi-automated MA and ML quantification makes it promising for future applications in clinical settings such as the operating room, where obtaining results in short time is important. (C) 2015 World Federation for Ultrasound in Medicine & Biology.

关键词： Mitral valve Echocardiography Mitral annulus Mitral leaflets Block-matching graph-based segmentation Mitral valve quantification

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Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach

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MEDICAL PHYSICS 2016年第6期43卷 2948-2964页

作者： Beichel, Reinhard R. Van Tol, Markus Ulrich, Ethan J. Bauer, Christian Chang, Tangel Plichta, Kristin A. Smith, Brian J. Sunderland, John J. Graham, Michael M. Sonka, Milan Buatti, John M. Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Iowa Inst Biomed Imaging Iowa City IA 52242 USA Univ Iowa Dept Internal Med Iowa City IA 52242 USA Univ Iowa Dept Radiat Oncol Iowa City IA 52242 USA Univ Iowa Dept Biostat Iowa City IA 52242 USA Univ Iowa Dept Radiol Iowa City IA 52242 USA

Purpose: The purpose of this work was to develop, validate, and compare a highly computer- aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Results: segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical o

关键词： cancer segmentation FDG PET imaging graph-based segmentation optimal surface finding just-enough-interaction principle

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Unsupervised segmentation of Point Clouds From Buildings Using Hierarchical Clustering based on Gestalt Principles

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2018年第11期11卷 4270-4286页

作者： Xu, Yusheng Yao, Wei Tuttas, Sebastian Hoegner, Ludwig Stilla, Uwe Tech Univ Munich Dept Photogrammetry & Remote Sensing D-80333 Munich Germany Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China

segmentation is a fundamental step for parsing the point clouds of three-dimensional (3-D) scenes, which normally contain a wide variety of complex objects and structures with a large amount of points. In this paper, we propose a voxel-based point cloud segmentation method using Gestalt principles under a hierarchical clustering framework, allowing a completely automatic but parametric process for segmenting 3-D scenes of buildings. The voxel-based data structure can increase the efficiency and robustness of the segmentation process. By the use of Gestalt principles, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, which can be applied to general applications. The clustering of patches in our method is carried out on the basis of the local geometric information, which is modeled by the probabilistic formulation and solved by the graphical model. Experiments using terrestrial laser scanning dataset have demonstrated that our proposed method can achieve good results, especially for complex scenes and non-planar surfaces of objects. The quantitative comparison between our method and other representative segmentation methods (i.e., region growing, voxel-based incremental segmentation, locally convex connected patches, etc.) also confirms the effectiveness and efficiency of our method, with overall F-1 measures better than 0.66 for our datasets under complex situations of urban scenes with irregular shaped buildings.

关键词： Building segmentation Gestalt principles graph-based segmentation hierarchical clustering point cloud probabilistic framework

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A generalized graph reduction framework for interactive segmentation of large images

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 150卷 44-57页

作者： Gueziri, Houssem-Eddine McGuffin, Michael J. Laporte, Catherine Ecole Technol Super 1100 Notre Dame Ouest Montreal PQ H3C 1K3 Canada

The speed of graph-based segmentation approaches, such as random walker (RW) and graph cut (GC), depends strongly on image size. For high-resolution images, the time required to compute a segmentation based on user input renders interaction tedious. We propose a novel method, using an approximate contour sketched by the user, to reduce the graph before passing it on to a segmentation algorithm such as RW or GC. This enables a significantly faster feedback loop. The user first draws a rough contour of the object to segment. Then, the pixels of the image are partitioned into "layers" (corresponding to different scales) based on their distance from the contour. The thickness of these layers increases with distance to the contour according to a Fibonacci sequence. An initial segmentation result is rapidly obtained after automatically generating foreground and background labels according to a specifically selected layer;all vertices beyond this layer are eliminated, restricting the segmentation to regions near the drawn contour. Further foreground background labels can then be added by the user to refine the segmentation. All iterations of the graph-based segmentation benefit from a reduced input graph, while maintaining full resolution near the object boundary. A user study with 16 participants was carried out for RW segmentation of a multi-modal dataset of 22 medical images, using either a standard mouse or a stylus pen to draw the contour. Results reveal that our approach significantly reduces the overall segmentation time compared with the status quo approach (p < 0.01). The study also shows that our approach works well with both input devices. Compared to super-pixel graph reduction, our approach provides full resolution accuracy at similar speed on a high-resolution benchmark image with both RW and GC segmentation methods. However, graph reduction based on super-pixels does not allow interactive correction of clustering errors. Finally, our approach can be combined

关键词： Interactive segmentation User study graph-based segmentation graph reduction Random walker graph cuts

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Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic

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PATTERN RECOGNITION LETTERS 2006年第11期27卷 1239-1251页

作者： Duarte, Abraham Sanchez, Angel Fernandez, Felipe Montemayor, Antonio S. URJC ESCET Madrid 28933 Spain FI UPM Dept Tecnol Foton Madrid 28660 Spain

This paper proposes a new evolutionary region merging method in order to efficiently improve segmentation quality results. Our approach starts from an oversegmented image, which is obtained by applying a standard morphological watershed transformation on the original image. Next, each resulting region is represented by its centroid. The oversegmented image is described by a simplified undirected weighted graph, where each node represents one region and weighted edges measure the dissimilarity between pairs of regions (adjacent and non-adjacent) according to their intensities, spatial locations and original sizes. Finally, the resulting graph is iteratively partitioned in a hierarchical fashion into two subgraphs, corresponding to the two most significant components of the actual image, until a termination condition is met. This graph-partitioning task is solved by a variant of the min-cut problem (normalized cut) using a hierarchical social (HS) metaheuristic. We have efficiently applied the proposed approach to brightness segmentation on different standard test images, with good visual and objective segmentation quality results. (c) 2005 Published by Elsevier B.V.

关键词： evolutionary metaheuristics image segmentation watershed region merging graph-based segmentation hierarchical social algorithm

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Matching segmentation hierarchies

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2004年第3期18卷 397-424页

作者： Glantz, R Pelillo, M Kropatsch, WC Johns Hopkins Univ Dept Geog & Environm Engn Baltimore MD 21218 USA Univ Ca Foscari Venezia Dipartimento Informat I-30172 Venice Italy Vienna Tech Univ Inst Comp Aided Automat 183 2 Pattern Recognit & Image Proc Grp A-1040 Vienna Austria

When matching regions from "similar" images, one typically has the problem of missing counterparts due to local or even global variations of segmentation fineness. Matching segmentation hierarchies, however, not only increases the chances of finding counterparts, but also allows us to exploit the manifold constraints coming from the topological relations between any two regions in a hierarchy. To define the topological relations we represent a plane image I by a plane attributed graph G and derive a finite topology O from G. In particular, segmenting I corresponds to taking a topological minor of G which, in turn, is equivalent to coarsening O. Moreover, each finite topology involved is a coarsening of the standard topology on R-2. Then, we construct a weighted association graph G(A), the nodes of which represent potential matches and the edges of which indicate topological consistency with respect to O. Specifically, a maximal weight clique of G(A) corresponds to a topologically consistent mapping with maximal total similarity. To find "heavy" cliques, we extend a greedy pivoting-based heuristic to the weighted case. Experiments on pairs of stereo images, on a video sequence of a cluttered outdoor scene, and on a sequence of panoramic images demonstrate the effectiveness of our method.

关键词： graph-based segmentation structural matching topological relations between regions

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