Results 11 to 20 of about 10,393 (248)
TSSP-UNet: A Two-Stage Weakly Supervised Pathological Image Segmentation With Point Annotations. [PDF]
IET Syst BiolDeep convolutional neural networks excel at image segmentation but face challenges with complex instance training and high‐precision annotation acquisition. This study proposes TSSP‐UNet, a two‐stage weakly supervised segmentation approach: the first stage trains a segmentation network with constraint and attention mechanisms plus a feature aggregation
Wang S +5 more
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Hybrid superpixel segmentation [PDF]
2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2015 Superpixel over-segment image into meaningful clusters so that pixels in each cluster belong to one object. Many state-of-art superpixel algorithms have to make trade-offs between different concerns. As a result, algorithms that can produce good result in some situations fail in another.
Yu, Y, Lai, S, Liu, Y, Du, T
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Bioengineering, 2022 Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field segmentation methods reduce the image size to speed up the subsequent processing time.
Chien-Cheng Lee +3 more
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BASS: Boundary-Aware Superpixel Segmentation [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 This work is partly funded by the Spanish MINECO project RobInstruct TIN2014-58178-R, by the ERA-Net Chistera project I-DRESS PCIN-2015-147 and by the EU project AEROARMS H2020-ICT-2014-1-644271. A. Rubio is supported by the industrial doctorate grant 2015-DI-010 of the AGAUR.
Rubio, Antonio +3 more
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Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging ...
Liang Zhang +5 more
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Detection of Hypergranulation Tissue in Chronic Wound Images Using Artificial Intelligence Algorithms. [PDF]
Wound Repair RegenABSTRACT Hypergranulation in chronic wounds reflects impaired healing, leading to delayed recovery, increased risk of infection and higher treatment costs for healthcare systems. Despite its impact, hypergranulation is often misidentified in the early stages, hindering timely intervention. This study presents a deep learning‐based method to distinguish
Reifs D +3 more
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Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC
Brain Sciences, 2020 Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI).
Yu Wang, Qi Qi, Xuanjing Shen
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SAR Image Segmentation Based on Fisher Vector Superpixel Generation and Label Revision
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 This article addresses the problem of superpixel-bases segmentation of synthetic aperture radar (SAR) images. Most superpixel segmentation methods have difficulties in segmenting adjacent regions with similar gray values, due to only considering spatial ...
Ronghua Shang +5 more
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Superpixel Segmentation Using Gaussian Mixture Model [PDF]
IEEE Transactions on Image Processing, 2018 Superpixel segmentation algorithms are to partition an image into perceptually coherence atomic regions by assigning every pixel a superpixel label. Those algorithms have been wildly used as a preprocessing step in computer vision works, as they can enormously reduce the number of entries of subsequent algorithms.
Zhihua Ban, Jianguo Liu, Li Cao
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Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review. [PDF]
NMR BiomedManually segmenting brain tumors in magnetic resonance imaging is a time‐consuming task that requires years of professional experience and clinical expertise. We proposed a study, which contains a comprehensive review of the brain tumor segmentation techniques. It selects the effective approaches to better understand the AI applications for brain tumor
Bibi K +9 more
europepmc +2 more sources