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27th International Conference on Knowledge based and Intelligent Information and Engineering Sytems, KES 2023

作者： Popa, Maria Department of Computer Science Babes-Bolyai University Mihail Kogalniceanu 1 Cluj-Napoca400084 Romania

Brain tumours and strokes are increasingly common, even from a young age. For this reason preliminary screenings, such as MRI scans, are crucial steps in detecting the abnormalities and finding a treatment. Given the increasing number of people developing different brain problems, there is a need in developing automated segmentation systems. Regardless the various methods presented in the literature, the lack of ground truth images determines the researchers to move towards unsupervised techniques. This paper presents an modified graph-based unsupervised brain segmentation method that uses a minimal spanning tree to segment brain from non-brain tissue. The original method uses human intervention for node selection. The aim is to eliminate human intervention and reduce the computational time required for brain segmentation. The presented method was compared with the original one in two ways: by resizing and scaling the images, and using images without any pre-processing step. Experimental results were obtained on the NFBS dataset and in both experiments the proposed approach delivers better results, both visually and numerically. Performance measures have improved, leading to better overall segmentation. The new method increased the precision and dice coefficient by 20%, resulting in a more accurate segmentation. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： brain segmentation graph-based segmentation graph-cut image segmentation

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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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An 3D MRI Unsupervised graph-based Skull Stripping Algorithm

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Procedia Computer Science 2023年 225卷 1682-1690页

作者： Maria Popa

关键词： brain segmentation graph-based segmentation graph-cut image segmentation

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Character segmentation on Natural Images with graph-based Representation 26

Character Segmentation on Natural Images with Graph-Based Re...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Koksal, Ali Isik, Zerrin Izmir Yuksek Teknol Enstitusu Bilgisayar Muhendisligi Bolumu Izmir Turkey Dokuz Eylul Univ Bilgisayar Muhendisligi Bolumu Izmir Turkey

ISBN: (纸本)9781538615010

Computer vision approaches like shape based descriptors use silhouettes of objects in images. In this paper, a method to extract silhouettes of objects by segmenting images is proposed in order to describe them, especially characters that are obtained from natural images by using shape based descriptors. This method is binary segmentation approach that has a graph-based representation. Dominant intensity values of segments of an image and cut off intensity value to separate those segments are computed dynamically. Thus, characters that have similar dominant intensity value to the background can be segmented as well. Moreover, the performance of the proposed graph based method is compared with the performance of the global thresholding and it is observed that the success of the proposed method is better than the global thresholding.

关键词： graph-based segmentation Character segmentation Binary mage segmentation Object Silhouette Detection

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Instance segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and graph-based Optimization 10

Instance Segmentation of Trees in Urban Areas from MLS Point...

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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 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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Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期60卷 101945-101945页

作者： Ramadan, Hiba Lachqar, Chaymae Tairi, Hamid Univ Sidi Mohamed Ben Abdellah Fac Sci Dhar El Mahraz Dept Informat BP 1796 Atlas Fez 30000 Fez Morocco Univ Sidi Mohamed Ben Abdellah Fac Med & Pharm Fes 30000 Morocco

The development of computer aided diagnosis (CAD) systems for the task of tumor detection and segmentation in breast ultrasound (BUS) images presents one of the most active research fields. In this paper, a saliency-guided approach for fast and automatic tumor segmentation in BUS images is proposed. We explore the ability of the concept of saliency to emulate radiologist expertise for tumor lesion detection in BUS images. For that, we compute BUS image saliency to generate effective tumor seeds and background seeds. Then, a graph-based interactive image segmentation method is applied automatically using the generated seeds to extract tumor region. For more accurate segmentation, we propose a novel saliency score measure to apply a post-processing step for result refinement. Our results have been compared with other approaches in challenged datasets and demonstrated the effectiveness of our saliency-guided approach for tumor detection and segmentation using different evaluation metrics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Computer aided diagnosis Breast ultrasound Tumor detection Tumor segmentation Saliency detection graph-based segmentation Saliency score Active contours

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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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COMBINING FULLY CONVOLUTIONAL NETWORKS AND graph-based APPROACH FOR AUTOMATED segmentation OF CERVICAL CELL NUCLEI 14

COMBINING FULLY CONVOLUTIONAL NETWORKS AND GRAPH-BASED APPRO...

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Zhang, Ling Sonka, Milan Lu, Le Summers, Ronald M. Yao, Jianhua NIH Radiol & Imaging Sci Dept Bldg 10 Bethesda MD 20892 USA Univ Iowa Iowa Inst Biomed Imaging Iowa City IA USA Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

ISBN: (纸本)9781509011728

Cervical nuclei carry substantial diagnostic information for cervical cancer. Therefore, in automation-assisted reading of cervical cytology, automated and accurate segmentation of nuclei is essential. This paper proposes a novel approach for segmentation of cervical nuclei that combines fully convolutional networks (FCN) and graph-based approach (FCNG). FCN is trained to learn the nucleus high-level features to generate a nucleus label mask and a nucleus probabilistic map. The mask is used to construct a graph by image transforming. The map is formulated into the graph cost function in addition to the properties of the nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are also utilized to modify the local cost functions. The globally optimal path in the constructed graph is identified by dynamic programming. Validation of our method was performed on cell nuclei from Herlev Pap smear dataset. Our method shows a Zijdenbos similarity index (ZSI) of 0.92 +/- 0.09, compared to the best state-ofthe- art approach of 0.89 +/- 0.15. The nucleus areas measured by our method correlated strongly with the independent standard (r(2) = 0.91).

关键词： Deep learning FCN graph-based segmentation Pap smear

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