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Journal articles on the topic 'Ultrasound image segmentation'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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Noble, J. A., and D. Boukerroui. "Ultrasound image segmentation: a survey." IEEE Transactions on Medical Imaging 25, no. 8 (2006): 987–1010. http://dx.doi.org/10.1109/tmi.2006.877092.

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Cai, Lina, Qingkai Li, Junhua Zhang, Zhenghua Zhang, Rui Yang, and Lun Zhang. "Ultrasound image segmentation based on Transformer and U-Net with joint loss." PeerJ Computer Science 9 (October 20, 2023): e1638. http://dx.doi.org/10.7717/peerj-cs.1638.

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Background Ultrasound image segmentation is challenging due to the low signal-to-noise ratio and poor quality of ultrasound images. With deep learning advancements, convolutional neural networks (CNNs) have been widely used for ultrasound image segmentation. However, due to the intrinsic locality of convolutional operations and the varying shapes of segmentation objects, segmentation methods based on CNNs still face challenges with accuracy and generalization. In addition, Transformer is a network architecture with self-attention mechanisms that performs well in the field of computer vision. B

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Saeed, Jwan N. "A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES." Academic Journal of Nawroz University 9, no. 1 (2020): 1. http://dx.doi.org/10.25007/ajnu.v9n1a523.

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The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges assoc

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Orlando, Nathan, Igor Gyacskov, Derek J. Gillies, et al. "Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound." Physics in Medicine & Biology 67, no. 7 (2022): 074002. http://dx.doi.org/10.1088/1361-6560/ac5a93.

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Abstract Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image qua

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Yang, Jing, Ping Tang, Jie Chen, and Huaxiang Shen. "Application and Analysis of Imaging Characteristics of Four-Dimensional Ultrasound in the Diagnosis of Fetal Cleft Lip and Palate." Journal of Medical Imaging and Health Informatics 11, no. 1 (2021): 133–38. http://dx.doi.org/10.1166/jmihi.2021.3520.

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Objective: In this paper, four-dimensional (4D) ultrasound scanning technology and level-set-based image segmentation algorithm are used to diagnose fetal cleft lip and palate to improve the detection rate and diagnostic accuracy of fetal cleft lip and palate cleft. Methods: Fifty-six fetuses were collected, and their type-B ultrasonic examination was cleft lip and palate. Also, they were identified as cleft lip and palate deformities after delivery or induced labor. Two-dimensional (2D) and 4D ultrasound scans were performed, and an ultrasound image of the fetal face was obtained using a leve

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Dr. M. Renukadevi, S. Suganyadevi,. "SEGMENTATION OF KIDNEY STONE REGION IN ULTRA SOUND IMAGEBY USING REGION PARTITION AND MOUNTING SEGMENTATION ALGORITHM (RPM)." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 512–18. http://dx.doi.org/10.17762/itii.v9i1.164.

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One of the safest techniques for disease diagnosis which can be used in any part of the body is ultrasound imaging. The cost when compared with MRI, PET etc are higher than using ultra sound images is the one of the major reason. Further, it is an efficient technique for initial diagnosis and it is free from any radiation exposure. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney. Hence conducting study on this segmented image becomes easy with an efficient segmentation technique. In this paper Various algorithm

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Zhou, Peng, Zhangjing Wu, Yong Yu, Yuxuan Zhao, Dan Huang, and Min Zhang. "Research on thyroid nodule segmentation algorithm based on improved U-Net model." ITM Web of Conferences 77 (2025): 01019. https://doi.org/10.1051/itmconf/20257701019.

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This research proposes an image segmentation model based on an improved U-Net network (rcU-Net) in response to the phenomena of misinterpretation and missed diagnosis in the process of artificial diagnosis and screening due to the variability of thyroid nodule size and unclear edges in ultrasound images. This paper uses the TN3K dataset as the experimental dataset. The superiority of the proposed model is validated through comparative experiments and ablation experiments. Experimental results show that the proposed model achieves an accuracy of 95.61%, an AUC of 90.67%, a specificity of 98.12%

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Carriere, Jay, Ron Sloboda, Nawaid Usmani, and Mahdi Tavakoli. "Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images." Applied Sciences 12, no. 6 (2022): 2994. http://dx.doi.org/10.3390/app12062994.

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Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation algorithm is proposed. The prostate contouring system presented here is based on a novel superpixel algorithm, whereby pixels in the ultrasound image are grouped into superpixel regions that are optimized based on statistical similarity measures, so that the various structures within the ultrasound image can be differ

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Zhang, Yingtao, Min Xian, Heng-Da Cheng, et al. "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation." Healthcare 10, no. 4 (2022): 729. http://dx.doi.org/10.3390/healthcare10040729.

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Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/2020jastt1328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challengin

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/jastt20201328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challengin

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (2020): 78–91. http://dx.doi.org/10.38094/jastt1328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challengin

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Khan, Muhammad Salim, Laiba Saqib, Zahir Shah, Haider Ali, and Ahmad Alshehri. "Efficient Echocardiographic Image Segmentation." Mathematical Problems in Engineering 2022 (September 10, 2022): 1–5. http://dx.doi.org/10.1155/2022/1754291.

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In this paper, we propose an improved region-based active contour method based on the development of a novel signed pressure force (SPF) function. To obtain the required boundary, the method is applied to the echocardiographic images. Ultrasound image segmentation is particularly challenging due to speckle noise, low contrast, and intensity inhomogeneity. Because of these factors, segmenting echocardiographic images is a difficult task. All of these issues are addressed by the proposed model, which detects the true boundary without any noise. The proposed model is more robust, effective, and a

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Shen, Jiaqi, Fangfang Huang, and Myers Ulrich. "Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1892–98. http://dx.doi.org/10.1166/jmihi.2020.3119.

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Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data a

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S, Sheela, and Sumathi M. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. https://doi.org/10.17485/IJST/v13i39.1602.

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Abstract <strong>Objective:</strong> To achieve the accurate segmentation of ovarian cyst from the ultrasound images. <strong>Method:</strong> Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enh

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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.

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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thres

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Zifan, Ali, Katelyn Zhao, Madilyn Lee, et al. "Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation." Diagnostics 15, no. 2 (2025): 117. https://doi.org/10.3390/diagnostics15020117.

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Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep lea

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Jeba Shiney, O., J. Amar Pratap Singh, and Priestly Shan B. "EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER." Biomedical & Pharmacology Journal 12, no. 3 (2019): 1135–39. http://dx.doi.org/10.13005/bpj/1741.

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Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contras

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Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, et al. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.

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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral

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Noble, J. A. "Ultrasound image segmentation and tissue characterization." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 224, no. 2 (2009): 307–16. http://dx.doi.org/10.1243/09544119jeim604.

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Krivanek, A., and M. Sonka. "Ovarian ultrasound image analysis: follicle segmentation." IEEE Transactions on Medical Imaging 17, no. 6 (1998): 935–44. http://dx.doi.org/10.1109/42.746626.

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Archip, Neculai, Robert Rohling, Peter Cooperberg, and Hamid Tahmasebpour. "Ultrasound image segmentation using spectral clustering." Ultrasound in Medicine & Biology 31, no. 11 (2005): 1485–97. http://dx.doi.org/10.1016/j.ultrasmedbio.2005.07.005.

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Huang, Qinghua, Yaozhong Luo, and Qiangzhi Zhang. "Breast ultrasound image segmentation: a survey." International Journal of Computer Assisted Radiology and Surgery 12, no. 3 (2017): 493–507. http://dx.doi.org/10.1007/s11548-016-1513-1.

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Maolood, Ismail Yaqub, Yahya Eneid Abdulridha Al-Salhi, and Songfeng Lu. "Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm." Open Medicine 13, no. 1 (2018): 374–83. http://dx.doi.org/10.1515/med-2018-0056.

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AbstractIn this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentat

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Khaledyan, Donya, Thomas J. Marini, Timothy M. Baran, Avice O’Connell, and Kevin Parker. "Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet." PLOS ONE 18, no. 12 (2023): e0289195. http://dx.doi.org/10.1371/journal.pone.0289195.

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performan

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Holland, Lawrence, Sofia I. Hernandez Torres, and Eric J. Snider. "Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images." Bioengineering 11, no. 2 (2024): 128. http://dx.doi.org/10.3390/bioengineering11020128.

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Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bo

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Sheela, S. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. http://dx.doi.org/10.17485/ijst/v13i39.1602.

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Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficie

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Bargsten, Lennart, Katharina A. Riedl, Tobias Wissel, et al. "Deep learning for calcium segmentation in intravascular ultrasound images." Current Directions in Biomedical Engineering 7, no. 1 (2021): 96–100. http://dx.doi.org/10.1515/cdbme-2021-1021.

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Abstract Knowing the shape of vascular calcifications is crucial for appropriate planning and conductance of percutaneous coronary interventions. The clinical workflow can therefore benefit from automatic segmentation of calcified plaques in intravascular ultrasound (IVUS) images. To solve segmentation problems with convolutional neural networks (CNNs), large datasets are usually required. However, datasets are often rather small in the medical domain. Hence, developing and investigating methods for increasing CNN performance on small datasets can help on the way towards clinically relevant re

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Pregitha R., Eveline, Vinod Kumar R. S., and Ebbie Selvakumar C. "FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET." International journal of electrical and computer engineering systems 14, no. 10 (2023): 1141–49. http://dx.doi.org/10.32985/ijeces.14.10.7.

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Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, t

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Bargsten, Lennart, Silas Raschka, and Alexander Schlaefer. "Capsule networks for segmentation of small intravascular ultrasound image datasets." International Journal of Computer Assisted Radiology and Surgery 16, no. 8 (2021): 1243–54. http://dx.doi.org/10.1007/s11548-021-02417-x.

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Abstract Purpose Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule netw

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Chang, Chenkai, Fei Qi, Chang Xu, Yiwei Shen, and Qingwu Li. "A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation." Mathematical Biosciences and Engineering 21, no. 1 (2023): 1038–57. http://dx.doi.org/10.3934/mbe.2024043.

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<abstract><p><italic>Objectives:</italic> We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks. <italic>Method:</italic> We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images wit

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Wang, Xinyu, Zhengqi Chang, Qingfang Zhang, Cheng Li, Fei Miao, and Gang Gao. "Prostate Ultrasound Image Segmentation Based on DSU-Net." Biomedicines 11, no. 3 (2023): 646. http://dx.doi.org/10.3390/biomedicines11030646.

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In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-N

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Darvish, Arman, and Shahryar Rahnamayan. "Optimal Parameter Setting of Active-Contours Using Differential Evolution and Expert-Segmented Sample Image." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 6 (2012): 677–86. http://dx.doi.org/10.20965/jaciii.2012.p0677.

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Generally, tissue extraction (segmentation) is one of the most challenging tasks in medical image processing. Inaccurate segmentation propagates errors to the subsequent steps in the image processing chain. Thus, in any image processing chain, the role of segmentation is in fact critical because it has a significant impact on the accuracy of the final results, such as those of feature extraction. The appearance of variant noise types makes medical image segmentation a more complicated task. Thus far, many approaches for image segmentation have been proposed, including the well-known active con

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Huang, Kuan, Yingtao Zhang, Heng-Da Cheng, and Ping Xing. "Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction." Healthcare 10, no. 12 (2022): 2480. http://dx.doi.org/10.3390/healthcare10122480.

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Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully under

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Wang, Yanwei, Junbo Ye, Tianxiang Wang, Jingyu Liu, Hao Dong, and Xin Qiao. "Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–15. http://dx.doi.org/10.1155/2022/1752390.

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To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified b

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Yun, Ting, Yi Qing Xu, and Lin Cao. "Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features." Applied Mechanics and Materials 239-240 (December 2012): 104–14. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.104.

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The research is aimed at the development of an image processing system for classification of pathological area for medical images obtained from computed tomography (CT) scans. We proposed a novel semi-supervised image segmentation method based on the curvelet transform and SVM classfication. Firstly, through curvelet transform ultrasound images were decomposed into different directions and scales, the main distribution curvelet coefficients were extracted by cauchy model to reduce the algorithm time complexity, after inverse curvelet transform to obtaine a series of feature vectors from main d

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Dašić, Lazar, Nikola Radovanović, Tijana Šušteršič, Anđela Blagojević, Leo Benolić, and Nenad Filipović. "Patch-based Convolutional Neural Network for Atherosclerotic Carotid Plaque Semantic Segmentation." Ipsi Transactions on Internet research 18, no. 1 (2022): 56–61. http://dx.doi.org/10.58245/ipsi.tir.22jr.10.

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Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using

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Zhao, Yuan, Mingjie Jiang, Wai Sum Chan, and Bernard Chiu. "Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness." Bioengineering 10, no. 10 (2023): 1217. http://dx.doi.org/10.3390/bioengineering10101217.

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Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation ti

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Ardhianto, Peter, Jen-Yung Tsai, Chih-Yang Lin, et al. "A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images." Applied Sciences 11, no. 9 (2021): 4021. http://dx.doi.org/10.3390/app11094021.

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Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and s

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Dilna, Kaitheri Thacharedath, and Duraisamy Jude Hemanth. "Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus." Open Computer Science 11, no. 1 (2021): 399–410. http://dx.doi.org/10.1515/comp-2020-0140.

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Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentatio

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