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Literatura académica sobre el tema "Ultrasound image segmentation"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Ultrasound image segmentation"

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J. Hemalatha, R., Dr V. Vijaybaskar, A. Josephin Arockia Dhivya, and . "Early detection of joint abnormalities from ultrasound images." International Journal of Engineering & Technology 7, no. 2.25 (2018): 105. http://dx.doi.org/10.14419/ijet.v7i2.25.16569.

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Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound

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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (2023): 2307. http://dx.doi.org/10.3390/s23042307.

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3),

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Bao, Junxiao, Cuilin Bei, Xiang Zheng, and Jinli Wang. "Deep Learning Algorithm in Biomedical Engineering in Intelligent Automatic Processing and Analysis of Sports Images." Wireless Communications and Mobile Computing 2022 (July 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/3196491.

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In order to improve the detection and identification ability of sports injury ultrasound medicine, a segmentation method of sports injury ultrasound medical image based on local features is proposed, and the research on the sports injury ultrasound medical detection and identification ability is carried out. Methods of the sports injury ultrasound medical image segmentation model are established; the sports injury ultrasound medical image information is enhanced by using the sports skeletal muscle block matching technology; the image features are extracted; and the characteristics of sports in

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Shao, Liping, Zubang Zhou, Hongmei Wu, Jinrong Ni, and Shulan Li. "Modeling of Hidden Markov in Ultrasound Image-Assisted Diagnosis." Journal of Healthcare Engineering 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/5597591.

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Different segmentation of lung nodules using the same segmentation algorithm can easily lead to excessive segmentation errors. Therefore, it is necessary to design an effective segmentation algorithm to improve image segmentation accuracy. Based on the hidden Markov model, this study processed the ultrasound images of pulmonary nodules to improve their diagnostic results. At the same time, this study was combined with the ultrasound image of lung nodules to process the ultrasound images. In addition, this study combines the convex hull algorithm for image processing, uses the improved vector m

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Sree, S. Jayanthi, and C. Vasanthanayaki. "Ultrasound Fetal Image Segmentation Techniques: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (2018): 52–60. http://dx.doi.org/10.2174/1573405613666170622115527.

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Background: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. </P><P> Discussion: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. Conclusion: This paper presents a review of these segmentation

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Wang, Yuxin, Jialiang Zhang, Jiangning Han, et al. "Ovarian Ultrasound Image Segmentation Algorithm with Fused Multi-Scale Features." Critical Reviews in Biomedical Engineering 53, no. 1 (2025): 47–57. http://dx.doi.org/10.1615/critrevbiomedeng.v53.i1.40.

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Ultrasound imaging technology plays a vital role in medical imaging. Ovarian ultrasound image segmentation is challenging due to the wide variation in lesion sizes caused by the cancer detection period and individual differences, as well as the noise from reflected wave interference. To address these challenges, we propose an innovative algorithm for ovarian ultrasound image segmentation that incorporates multi-scale features. This algorithm effectively processes image data with varying scales. By introducing a skip connection structure, the shallow image features are preserved. Additionally,

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Suri, Jasjit, Yujun Guo, Cara Coad, Tim Danielson, Idris Elbakri, and Roman Janer. "Image Quality Assessment via Segmentation of Breast Lesion in X-ray and Ultrasound Phantom Images from Fischer's Full Field Digital Mammography and Ultrasound (FFDMUS) System." Technology in Cancer Research & Treatment 4, no. 1 (2005): 83–92. http://dx.doi.org/10.1177/153303460500400111.

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Fischer has been developing a fused full-field digital mammography and ultrasound (FFDMUS) system funded by the National Institute of Health (NIH). In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (i) to assess the image quality of X-ray and ultrasound images; (ii) to register multi-modality images; and (iii) to establish an automatic lesion detection methodology to assist the radiologist. In this paper we developed lesion segmentation strategies for ultrasound and X-ray images acquired using F

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Sun, Jingmeng, and Yifei Liu. "Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion." Mathematical Problems in Engineering 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/4825720.

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Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estima

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Xiao, Xiaolong, Jianfeng Zhang, Yuan Shao, et al. "Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges." Sensors 25, no. 8 (2025): 2361. https://doi.org/10.3390/s25082361.

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The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods.

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Wu, Shibin, Shaode Yu, Ling Zhuang, et al. "Automatic Segmentation of Ultrasound Tomography Image." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2059036.

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Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated

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Tesis sobre el tema "Ultrasound image segmentation"

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Gong, Lixin. "Prostate ultrasound image segmentation and registration /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5937.

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Rohlén, Robin. "Segmentation of motor units in ultrasound image sequences." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126896.

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The archetypal modern comic book superhero, Superman, has two superpowers of interest: the ability to see into objects and the ability to see distant objects. Now, humans possess these powers as well, due to the medical ultrasound imaging and sound navigation. Ultrasound, a type of sound we cannot hear, has enabled us to see a world otherwise invisible to us. Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by so called motor units which are the sm

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Badiei, Sara. "Prostate segmentation in ultrasound images using image warping and ellipsoid fitting." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31737.

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This thesis outlines an algorithm for 2D and 3D semi-automatic segmentation of the prostate from B-mode trans-rectal ultrasound (TRUS) images. In semi-automatic segmentation, a computer algorithm outlines the boundary of the prostate given a few initialization points. The algorithm is designed for prostate brachytherapy and has the potential to: i) replace pre-operative manual segmentation, ii) enable intra-operative segmentation, and iii) be integrated into a visualization tool for training residents. The segmentation algorithm makes use of image warping to make the 2D prostate boundary

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Quartararo, John David. "Semi-Automated Segmentation of 3D Medical Ultrasound Images." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/155.

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A level set-based segmentation procedure has been implemented to identify target object boundaries from 3D medical ultrasound images. Several test images (simulated, scanned phantoms, clinical) were subjected to various preprocessing methods and segmented. Two metrics of segmentation accuracy were used to compare the segmentation results to ground truth models and determine which preprocessing methods resulted in the best segmentations. It was found that by using an anisotropic diffusion filtering method to reduce speckle type noise with a 3D active contour segmentation routine using the leve

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Ghose, Soumya. "Robust image segmentation applied to magnetic resonance and ultrasound images of the prostate." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/98524.

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Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonance images (MRI) facilitates volume estimation, multi-modal image registration, surgical planing and image guided prostate biopsies. The objective of this thesis is to develop computationally efficient prostate segmentation algorithms in both TRUS and MRI image modalities. In this thesis we propose a probabilistic learning approach to achieve a soft classification of the prostate for automatic initialization and evolution of a deformable model for prostate segmentation. Two deformable models are developed for the TRUS s

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Wen, Shuangyue. "Automatic Tongue Contour Segmentation using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38343.

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Ultrasound is one of the primary technologies used for clinical purposes. Ultrasound systems have favorable real-time capabilities, are fast and relatively inexpensive, portable and non-invasive. Recent interest in using ultrasound imaging for tongue motion has various applications in linguistic study, speech therapy as well as in foreign language education, where visual-feedback of tongue motion complements conventional audio feedback. Ultrasound images are known to be difficult to recognize. The anatomical structure in them, the rapidity of tongue movements, also missing segments in some f

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Zhao, Ningning. "Inverse problems in medical ultrasound images - applications to image deconvolution, segmentation and super-resolution." Phd thesis, Toulouse, INPT, 2016. http://oatao.univ-toulouse.fr/16613/1/Zhao.pdf.

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In the field of medical image analysis, ultrasound is a core imaging modality employed due to its real time and easy-to-use nature, its non-ionizing and low cost characteristics. Ultrasound imaging is used in numerous clinical applications, such as fetus monitoring, diagnosis of cardiac diseases, flow estimation, etc. Classical applications in ultrasound imaging involve tissue characterization, tissue motion estimation or image quality enhancement (contrast, resolution, signal to noise ratio). However, one of the major problems with ultrasound images, is the presence of noise, having the form

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von, Lavante Etienne. "Segmentation and sizing of breast cancer masses with ultrasound elasticity imaging." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81225f61-6b83-405b-aed5-17b316ed586a.

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Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel seg

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Rackham, Thomas. "Ultrasound segmentation tools and their application to assess fetal nutritional health." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:5d102b18-dd32-4004-8aa5-b04242139daa.

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Maternal diet can have a great impact on the health and development of the fetus. Poor fetal nutrition has been linked to the development of a set of conditions in later life, such as coronary heart disease, type 2 diabetes and hypertension, while restricted growth can result in hypogylcemia, hypocalcemia, hypothermia, polycythemia, hyperbilirubinemia and cerebral palsy. High alcohol consumption during pregnancy can result in Fetal Alcohol Syndrome, a condition that can cause growth retardation, lowered intelligence and craniofacial defects. Current biometric assessment of the fetus involves s

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Navarrete, Hurtado Hugo Ariel. "Electromagnetic models for ultrasound image processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/398235.

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Speckle noise appears when coherent illumination is employed, as for example Laser, Synthetic Aperture Radar (SAR), Sonar, Magnetic Resonance, X-ray and Ultrasound imagery. Backscattered echoes from the randomly distributed scatterers in the microscopic structure of the medium are the origin of speckle phenomenon, which characterizes coherent imaging with a granular appearance. It can be shown that speckle noise is of multiplicative nature, strongly correlated and more importantly, with non-Gaussian statistics. These characteristics differ greatly from the traditional assumption of white add

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Libros sobre el tema "Ultrasound image segmentation"

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wasson, vikas, and gurinder kaur. Novel Approach for Thyroid Segmentation of Ultrasound Images Based on Neural Networks. Independently Published, 2018.

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Evaluation of Segmentation for Bone Structures in 3D Rendering of Ultrasound Residual Limb Images. Storming Media, 1996.

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Capítulos de libros sobre el tema "Ultrasound image segmentation"

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Stojanovski, David, Uxio Hermida, Pablo Lamata, Arian Beqiri, and Alberto Gomez. "Echo from Noise: Synthetic Ultrasound Image Generation Using Diffusion Models for Real Image Segmentation." In Simplifying Medical Ultrasound. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44521-7_4.

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AbstractWe propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthe

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Kolář, Radim, and Jiří Kozumplík. "Fuzzy Approach in Ultrasound Image Segmentation." In Computational Intelligence. Theory and Applications. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_92.

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Li, Haoming, Xin Yang, Jiamin Liang, et al. "Contrastive Rendering for Ultrasound Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_54.

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Archip, Neculai, Robert Rohling, Peter Cooperberg, Hamid Tahmasebpour, and Simon K. Warfield. "Spectral Clustering Algorithms for Ultrasound Image Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_106.

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Talebi, Mohammad, and Ahmad Ayatollahi. "Genetic Snake for Medical Ultrasound Image Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21596-4_6.

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Casaburi, D., L. D’Amore, L. Marcellino, and A. Murli. "A Motion-Aided Ultrasound Image Sequence Segmentation." In Numerical Mathematics and Advanced Applications 2009. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11795-4_22.

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Shah, Nemil, Jay Bhatia, Nimit Vasavat, Kanishk Shah, and Pratik B. Kanani. "Ultrasound Nerve Image Segmentation Using Attention Mechanism." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5974-7_63.

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Yang, Xin, Haoran Dou, Ran Li, et al. "Generalizing Deep Models for Ultrasound Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_57.

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Li, W., J. G. Bosch, Y. Zhong, et al. "Image Segmentation and 3D Reconstruction of Intravascular Ultrasound Images." In Acoustical Imaging. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2958-3_65.

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Yang, Xin, Lequan Yu, Shengli Li, et al. "Towards Automatic Semantic Segmentation in Volumetric Ultrasound." In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66182-7_81.

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Actas de conferencias sobre el tema "Ultrasound image segmentation"

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Cai, Junyu, Xiangjin Zeng, Qiming Zheng, and Hetao Ma. "Ultrasound Placenta Image Segmentation Based on TransCDnet." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957895.

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Lin, Tzu-Han, Daehan Kwak, and Kuan Huang. "Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781719.

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Li, Hao, Baris Oguz, Gabriel Arenas, et al. "PRISM Lite: a lightweight model for interactive 3D placenta segmentation in ultrasound." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047410.

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Che, Hui, Zihan Ji, Changhao Ju, and Jian Wu. "DCFSeg-Net for Liver Ultrasound Sequence Segmentation in Ultrasound-Guided Surgery." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906195.

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Yangue, Emmanuel, Yuxuan Li, Ashish Ranjan, and Chenang Liu. "An Adaptive Image Segmentation Approach for Tumor Region Identification in Ultrasound Images." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782614.

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Luong, Huu-Phong, Hoang-Son Bui, Nam-Khanh Nguyen, et al. "SovaSeg-Net: Scale Invariant Ovarian Tumors Segmentation from Ultrasound Images." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647995.

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Wang, Dongfang, Tao Zhou, and Jian Yang. "Hybrid-Frequency Feature Evolution Network for Endoscopic Ultrasound Image Segmentation." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980661.

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Nugroho, Anan, Risanuri Hidayat, and Hanung Adi Nugroho. "Thyroid Ultrasound Image Segmentation: A Review." In 2019 5th International Conference on Science and Technology (ICST). IEEE, 2019. http://dx.doi.org/10.1109/icst47872.2019.9166443.

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Bass, Vivian, Julieta Mateos, Ivan M. Rosado-Mendez, and Jorge Márquez. "Ultrasound image segmentation methods: A review." In PROCEEDINGS OF THE XVI MEXICAN SYMPOSIUM ON MEDICAL PHYSICS. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0051110.

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Yuan, Baichuan, Yoni Dukler, Long Zhao, et al. "Automatic valve segmentation in cardiac ultrasound time series data." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293255.

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Informes sobre el tema "Ultrasound image segmentation"

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Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA). Chair Andrew Buckler, Luca Saba, and Uwe Joseph Schoepf. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20230328.

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The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts (1). Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes (2). However, many definitions and cut-offs are present

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Saba, Luca, and Uwe Joseph Schoepf. Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA) - Maintenance version June 2024. Chair Andrew Buckler. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2024. http://dx.doi.org/10.1148/qiba/202406.

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The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts. Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes. However, many definitions and cut-offs are present in the

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He, Ping, and Jun Zheng. Segmentation of TIBIA Bone in Ultrasound Images Using Active Shape Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada412425.

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