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graph-based segmentation of abnormal nuclei in cervical cytology

COMPUTERIZED MEDICAL IMAGING AND graphICS 2017年 56卷 38-48页

作者： Zhang, Ling Kong, Hui Liu, Shaoxiong Wang, Tianfu Chen, Siping Sonka, Milan Shenzhen Univ Guangdong Key Lab Biomed Measurements & Ultrasoun Sch Med Dept Biomed Engn Shenzhen 518060 Peoples R China Univ Iowa Iowa Inst Biomed Imaging Iowa City IA 52242 USA Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Peoples Hosp Nanshan Dist Dept Pathol Shenzhen 518052 Peoples R China

A general method is reported for improving the segmentation of abnormal cell nuclei in cervical cytology images. In automation-assisted reading of cervical cytology, one of the essential steps is the segmentation of nuclei. Despite some progress, there is a need to improve the sensitivity, particularly the segmentation of abnormal nuclei. Our method starts with pre-segmenting the nucleus to define the coarse center and size of nucleus, which is used to construct a graph by image unfolding that maps ellipse-like border in the Cartesian coordinate system to lines in the polar coordinate system. The cost function jointly reflects properties of nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are utilized to modify the local cost functions. The globally optimal path in the constructed graph is then identified by dynamic programming with an iterative approach ensuring an optimal closed contour. Validation of our method was performed on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained manual liquid-based cytology (HEMLBC). Compared with five state-of-the-art approaches, our graph-search based method shows superior performance. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Image cytometry Cervical cells Abnormal nuclei segmentation graph-based segmentation

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graph-based segmentation of Lymph Nodes in CT Data

Graph-Based Segmentation of Lymph Nodes in CT Data

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6th International Symposium on Visual Computing

作者： Wang, Yao Beichel, Reinhard Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

ISBN: (纸本)9783642172731

The quantitative assessment of lymph node size plays an important role in treatment of diseases like cancer. In current clinical practice, lymph nodes are analyzed manually based on very rough measures of long and/or short axis length, which is error prone. In this paper we present a graph-based lymph node segmentation method to enable the computer-aided three-dimensional (3D) assessment of lymph node size. Our method has been validated on 22 cases of enlarged lymph nodes imaged with X-ray computed tomography (CT). For the signed and unsigned surface positioning error, the mean and standard deviation was 0.09+/-0.17 mm and 0.47+/-0.08 mm, respectively. On average, 5.3 seconds were required by our algorithm for the segmentation of a lymph node.

关键词： lymph node segmentation graph-based segmentation

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graph-based segmentation for Colored 3D Laser Point Clouds

Graph-based Segmentation for Colored 3D Laser Point Clouds

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Strom, Johannes Richardson, Andrew Olson, Edwin Univ Michigan Dept Comp Sci & Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9781424466757

We present an efficient graph-theoretic algorithm for segmenting a colored laser point cloud derived from a laser scanner and camera. segmentation of raw sensor data is a crucial first step for many high level tasks such as object recognition, obstacle avoidance and terrain classification. Our method enables combination of color information from a wide field of view camera with a 3D LIDAR point cloud from an actuated planar laser scanner. We extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Our combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone. We verify our approach on both indoor and outdoor scenes.

关键词： 3D LIDAR point cloud actuated planar laser scanner camera cameras colored 3D laser point clouds graph theory graph-based segmentation image segmentation image sensors optical radar optical scanners spatial features

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Multistage graph-based segmentation of thoracoscopic images

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COMPUTERIZED MEDICAL IMAGING AND graphICS 2006年第8期30卷 437-446页

作者： Bilodeau, Guillaume-Alexandre Shu, Yueyun Cheriet, Farida Ecole Polytech Montreal PQ H3C 3A7 Canada Hop St Justine Montreal PQ H3T 1C5 Canada

This paper presents a graph-based segmentation method using multiple criteria in successive stages to segment thoracoscopic images acquired during a diskectomy procedure commonly used for thoracoscopic anterior release and fusion for scoliosis treatment. Starting with image preprocessing, including Gaussian smoothing, brightness and contrast enhancement, and histogram thresholding, a standard graph-based method is applied to produce a coarse segmentation of thoracoscopic images. Next, regions are further merged in a multistage graph-based process based on features like grey-level similarity, region size and common edge length. Experimental results show that our approach achieves good spatial coherence, accurate. edge location and appropriate segmentation of the regions of interest from a sequence of thoracoscopic images. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： thoracic diskectomy thoracoscopic images video-assisted thoracoscopic surgery (VATS) graph-based segmentation region merging

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Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Apr.期126卷 245-260页

作者： Alshehhi, Rasha Marpu, Prashanth Reddy Masdar Inst Sci & Technol Inst Ctr Smart & Sustainable Syst Abu Dhabi U Arab Emirates

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Road networks graph-based segmentation Gabor Morphological filtering Hierarchical merging and splitting

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Multi-layered tree crown extraction from LiDAR data using graph-based segmentation

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 170卷 105213-105213页

作者： Dong, Tianyang Zhang, Xinpeng Ding, Zhanfeng Fan, Jing Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

With the development of Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) technology, extracting tree crowns from LiDAR data and infering their geometrical features are becoming more available for everyone. Although the forest has a significant stratification phenomenon in the vertical direction, the existing individual tree detection methods generally aimed at solving overstory trees detection. As a result, the understory trees cannot be effectively extracted. To effectively detect individual tree from multi-layer forests, a multi-layered tree extraction method using a graph-based segmentation algorithm was proposed in this study. First, using the graph-based segmentation algorithm delineates the canopy of the overstory tree on the canopy height model (CHM) generated by LiDAR data. Then, using the sliding window detection method extracts the LiDAR data of understory trees. Finally, the information of understory trees is extracted by the graph-based segmentation algorithm. To verify the performance of the proposed method, this study selected six experimental plots from two research areas. According to the result of our method, the highest matching score and average score for overstory trees reached 91.3 and 86.3;the highest matching score and average score for understory trees reached 78.1 and 63.2. Compared with other methods, our method has better detection results. The experimental results show that the proposed method can extract the understory and overstory trees effectively, thus improving the accuracy of individual tree extraction in multi-layered forests.

关键词： LiDAR Individual tree detection graph-based segmentation Tree structure

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OBJECT DETECTION USING HIERARCHICAL graph-based segmentation

OBJECT DETECTION USING HIERARCHICAL GRAPH-BASED SEGMENTATION

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

作者： Kim, Jungho Choi, Byeongho Kweon, In-So Multimedia IP Research Center KETI Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781479903566

Object detection in real images or videos is challenging because the shapes and sizes of objects vary significantly according to their poses, camera viewing direction, and partial occlusion. Previous detection methods employ sliding-window-based schemes that scan windows across an image, requiring many differently shaped windows to capture shape and size variation. In order to solve this problem, we propose an object detection method using hierarchical graph-based segmentation: color-consistent parts are obtained by part-level segmentation and category-consistent regions are found using object-level segmentation. Thus we can avoid scanning a lot of windows across whole images by using part-level segmentation and robustly detect the objects of various shapes and sizes by using object-level segmentation. In addition, we evaluate detection performance using various classifiers with our detection approach.

关键词： Object detection Object classification graph-based segmentation

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FULL-RANGE AFFINITIES FOR graph-based segmentation

FULL-RANGE AFFINITIES FOR GRAPH-BASED SEGMENTATION

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20th IEEE International Conference on Image Processing (ICIP)

作者： Li, Xiang Jin, Lianghai Song, Enmin Li, Lei Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil Engn & Mech Wuhan 430074 Peoples R China

ISBN: (纸本)9781479923410

graph-based segmentation has become a major trend in image segmentation. A key issue in graph-based segmentation is how to build the affinity matrix. Among the previous methods, many successful ones only compute the pairwise affinities between adjacent pixels and superpixels without considering the nonadjacent ones. Thus, they often obtain unsatisfactory results when foreground is cut into several nonadjacent parts by background or shadows. In this paper, we propose a full-range affinities learning method for graph-based segmentation. Our method computes the affinities both between adjacent pixels and nonadjacent pixels, which are inversely proportional to the shortest connectivity paths. The experimental results demonstrate the superiority of the proposed approach comparing with existing popular methods.

关键词： graph-based segmentation affinity learning

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graph-based IVUS segmentation With Efficient Computer-Aided Refinement

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2013年第8期32卷 1536-1549页

作者： Sun, Shanhui Sonka, Milan Beichel, Reinhard R. 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

A new graph-based approach for segmentation of luminal and external elastic lamina (EEL) surface of coronary vessels in gated 20 MHz intravascular ultrasound (IVUS) image sequences (volumes) is presented. The approach consists of a fully automated segmentation stage ("new automated" or NA) and a user-guided computer-aided refinement ("new refinement" or NR) stage. Both approaches are based on the LOGISMOS approach for simultaneous dual-surface graph-based segmentation. This combination allows the user to efficiently combine general information about IVUS image appearance and case-specific IVUS morphology and therefore deal with frequently occurring issues like calcified plaque-causing signal shadowing-and imaging artifacts. The automated segmentation stage starts with pre-segmenting the lumen to automatically define the lumen centerline, which is used to transform the segmentation task into a LOGISMOS-family graph optimization problem. Following the automated segmentation, the user can inspect the result and correct local or regional segmentation inaccuracies by (iteratively) providing approximate clues regarding the location of the desired surface locations. This expert information is utilized to modify the previously calculated cost functions, locally re-optimizing the underlying modified graph without a need to start the new optimization from scratch. Validation of our method was performed on 41 gated 20 MHz IVUS data sets for which an expert-defined independent standard was available. Resulting from the automated stage of the approach (NA), the mean and standard deviation of the root mean square area errors for the luminal and external elastic lamina surfaces were 1.12 +/- 0.67 mm(2) and 2.35 +/- 1.61 mm(2), respectively. Following the refinement stage (NR), the root mean square area errors significantly decreased to 0.82 +/- 0.44 mm(2) and 1.17 +/- 0.65 mm(2) for the same surfaces, respectively (p < 0.001 for both surfaces). The approach is delivering a previou

关键词： graph-based segmentation intravascular ultrasound segmentation refinement

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FULL-RANGE AFFINITIES FOR graph-based segmentation

FULL-RANGE AFFINITIES FOR GRAPH-BASED SEGMENTATION

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IEEE International Conference on Image Processing

作者： Xiang Li Lianghai Jin Enmin Song Lei Li School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 PR. China

ISBN: (纸本)9781479923427

关键词： graph-based segmentation affinity learning edge-based segmentation region segmentation texture-based segmentation Kinship image segmentation pixel segmentation segmentation Inverse Proportion WWOX gene Learning methods Neighbor

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