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Relevant bibliographies by topics / Kidney Segmentation –Active Contour

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Academic literature on the topic 'Kidney Segmentation –Active Contour'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Journal articles on the topic "Kidney Segmentation –Active Contour"

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Hsu, Wei-Yen, Chih-Cheng Lu, and Yuan-Yu Hsu. "Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model." Medicine 99, no. 47 (2020): e23083. http://dx.doi.org/10.1097/md.0000000000023083.

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He, Jinmei, Yuqian Zhao, Fan Zhang, and Feifei Hou. "An Unsupervised Computed Tomography Kidney Segmentation with Multi-Region Clustering and Adaptive Active Contours." Mathematics 12, no. 15 (2024): 2362. http://dx.doi.org/10.3390/math12152362.

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Kidney segmentation from abdominal computed tomography (CT) images is essential for computer-aided kidney diagnosis, pathology detection, and surgical planning. This paper introduces a kidney segmentation method for clinical contrast-enhanced CT images. First, it begins with shape-based preprocessing to remove the spine and ribs. Second, a novel clustering algorithm and an initial kidney selection strategy are utilized to locate the initial slices and contours. Finally, an adaptive narrow-band approach based on active contours is developed, followed by a clustering postprocessing to address is

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Al-Shamasneh, Ala’a R., Hamid A. Jalab, Palaiahnakote Shivakumara, Rabha W. Ibrahim, and Unaizah H. Obaidellah. "Kidney segmentation in MR images using active contour model driven by fractional-based energy minimization." Signal, Image and Video Processing 14, no. 7 (2020): 1361–68. http://dx.doi.org/10.1007/s11760-020-01673-9.

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Skalski, Andrzej, Katarzyna Heryan, Jacek Jakubowski, and Tomasz Drewniak. "Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints." Metrology and Measurement Systems 24, no. 1 (2017): 101–12. http://dx.doi.org/10.1515/mms-2017-0006.

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Abstract With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological su

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Zhang, Pin, Yanmei Liang, Shengjiang Chang, and Hailun Fan. "Kidney segmentation in CT sequences using graph cuts based active contours model and contextual continuity." Medical Physics 40, no. 8 (2013): 081905. http://dx.doi.org/10.1118/1.4812428.

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Vilimek, Dominik, Jan Kubicek, Adela Kloudova, et al. "Modeling of Kidney Stones from Ultrasound Images based on Hybrid Regional Segmentation with Active Contours." Acta Mechanica Slovaca 23, no. 4 (2020): 38–45. http://dx.doi.org/10.21496/ams.2020.014.

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MISHRA, AJAY K., and YIANNIS ALOIMONOS. "ACTIVE SEGMENTATION." International Journal of Humanoid Robotics 06, no. 03 (2009): 361–86. http://dx.doi.org/10.1142/s0219843609001784.

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The human visual system observes and understands a scene/image by making a series of fixations. Every "fixation point" lies inside a particular region of arbitrary shape and size in the scene which can either be an object or just a part of it. We define as a basic segmentation problem the task of segmenting that region containing the "fixation point". Segmenting the region containing the fixation is equivalent to finding the enclosing contour — a connected set of boundary edge fragments in the edge map of the scene — around the fixation. This enclosing contour should be a depth boundary. We pr

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Mustafa, Mahfuzah, Hana Najwa Omar Rashid, Nor Rul Hasma Abdullah, Rosdiyana Samad, and Dwi Pebrianti. "Mammography Image Segmentation: Chan-Vese Active Contour and Localised Active Contour Approach." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (2017): 577. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp577-583.

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<p>Breast cancer is one of the most common diseases diagnosed among female cancer patients. Early detection of breast cancer is needed to reduce the risk of fatality of this disease as no cure has been found yet for this illness. This research is conducted to improve the Gradient Vector Flow (GVF) Snake Active Contour segmentation technique in mammography segmentation. Segmentation of the mammogram image is done to segment lesions existence using Chan-Vese Active Contour and Localized Active Contour. Besides that, the effectiveness of these both methods are then compared and chosen to be

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Syaputri, Sintha, and Zulkarnain. "SEGMENTASI CITRA THORAX PARU-PARU MANUSIA DARI SINAR-X MENGGUNAKAN METODE KONTUR AKTIF." JOURNAL ONLINE OF PHYSICS 4, no. 2 (2019): 8–10. http://dx.doi.org/10.22437/jop.v4i2.7577.

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Research on medical image objects in the form of lung images of thoracic X-Rayis increasingly being developed because the information contained in medical images is used to analyze and determine the shape of the lungs. The process of normalization and image improvement is needed and continued with the segmentation process using the right method. The active snake contour method is used because it is resistant to the noise around the object. The research has been usedthe Matlab software GUI program version R2015a. The image through the initial preprocessing stage is converted into a grayscale im

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Jamaludin, Shahrizan, Ahmad Faisal Mohamad Ayob, Syamimi Mohd Norzeli, and Saiful Bahri Mohamed. "ADAPTIVE INITIAL CONTOUR AND PARTLY-NORMALIZATION ALGORITHM FOR IRIS SEGMENTATION OF BLURRY IRIS IMAGES." Journal of Information and Communication Technology 21, No.3 (2022): 411–35. http://dx.doi.org/10.32890/jict2022.21.3.5.

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Iris segmentation is a process to isolate the accurate iris region from the eye image for iris recognition. Iris segmentation on non-ideal and noisy iris images is accurate with active contour. Nevertheless, it is currently unclear on how active contour responds to blurry iris images or motion blur, which presents a significant obstacle in iris segmentation. Investigation on blurry iris images, especially on the initial contour position, is rarely published and must be clarified. Moreover, evolution or convergence speed remains a significant challenge for active contour as it segments the prec

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Dissertations / Theses on the topic "Kidney Segmentation –Active Contour"

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Judkovich, Michael. "An Active Contour Approach for 3D Thigh Muscle Segmentation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1618866341802777.

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Zhang, Jie. "A local likelihood active contour model of medical image segmentation." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1187087285.

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Zhang, Jie. "A Local Likelihood Active Contour Model for Medical Image Segmentation." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1187087285.

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Tsang, Po-Yan. "Multi-resolution Image Segmentation using Geometric Active Contours." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/907.

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Image segmentation is an important step in image processing, with many applications such as pattern recognition, object detection, and medical image analysis. It is a technique that separates objects of interests from the background in an image. Geometric active contour is a recent image segmentation method that overcomes previous problems with snakes. It is an attractive method for medical image segmentation as it is able to capture the object of interest in one continuous curve. The theory and implementation details of geometric active contours are discussed in this work. T

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Law, Wai Kong. "Segmentation of vessels using weighted local variances and an active contour model /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20LAW.

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Thieu, Quang Tung. "Segmentation by convex active contour models : application to skin lesion and medical images." Paris 13, 2013. http://www.theses.fr/2013PA132063.

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Wang, Yang. "Segmentation Guided Registration for Medical Images." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1126905907.

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Ananta, Soujanya Siddavaram. "Contour Extraction of Drosophila Embryos Using Active Contours in Scale Space." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1222.

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Contour extraction of Drosophila embryos is an important step to build a computational system for pattern matching of embryonic images which aids in the discovery of genes. Automatic contour extraction of embryos is challenging due to several image variations such as size, shape, orientation and neigh- boring embryos such as touching and non-touching embryos. In this thesis, we introduce a framework for contour extraction based on the connected components in the gaussian scale space of an embryonic image. The active contour model is applied on the images to refine embryo contours. Data cleanin

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Smestad, Ole Marius. "On the feasability of automatic segmentation with active contour models in image databases for shape extraction." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9223.

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<p>In this thesis the image segmentation system EDISON, was tested against an automatic version snake, which is an algorithm active contour models. The algorithms were tested against each to see if an automatic snake algorithm could be feasible for use in an image database for shape extraction. The conducted tests showed that EDISON yielded the best results, and that snake should be given further work before being considered.</p>

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Kienel, Enrico, and Guido Brunnett. "GPU-Accelerated Contour Extraction on Large Images Using Snakes." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200900358.

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Active contours have been proven to be a powerful semiautomatic image segmentation approach, that seems to cope with many applications and different image modalities. However, they exhibit inherent drawbacks, including the sensibility to contour initialization due to the limited capture range of image edges and problems with concave boundary regions. The Gradient Vector Flow replaces the traditional image force and provides an enlarged capture range as well as enhanced concavity extraction capabilities, but it involves an expensive computational effort and considerably increased memory require

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Book chapters on the topic "Kidney Segmentation –Active Contour"

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Raghotham Reddy, G., M. Mahesh Chandra, Kama Ramudu, and R. Rameshwar Rao. "Active Contour Based Color Image Segmentation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29216-3_20.

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Yan, Pingkun, and Ashraf A. Kassim. "MRA Image Segmentation with Capillary Active Contour." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566465_7.

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Krueger, Matthias, Patrice Delmas, and Georgy Gimel’farb. "Active Contour Based Segmentation of 3D Surfaces." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88688-4_26.

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Tatu, Aditya, and Sumukh Bansal. "A Novel Active Contour Model for Texture Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14612-6_17.

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Jain, Kavindra R., and N. C. Chauhan. "Segmentation of Dental Radiographs Using Active Contour Model." In Dental Image Analysis for Disease Diagnosis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14136-3_4.

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Zhang, Mo, Bin Dong, and Quanzheng Li. "Deep Active Contour Network for Medical Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59719-1_32.

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Soudani, Amira, and Ezzeddine Zagrouba. "Image Segmentation Based on Hybrid Adaptive Active Contour." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19644-2_13.

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Kumar, Ankit, and Subit K. Jain. "Fractional Coupled Active Contour Model for Image Segmentation." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2179-8_27.

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Yao, Heming, Craig Williamson, Jonathan Gryak, and Kayvan Najarian. "Brain Hematoma Segmentation Using Active Learning and an Active Contour Model." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17935-9_35.

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Li, Hongsheng, Tian Shen, Dimitrios Vavylonis, and Xiaolei Huang. "Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15705-9_11.

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Conference papers on the topic "Kidney Segmentation –Active Contour"

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Pallavi, P., S. P. Angeline Kirubha, Anish Kumar Patra, Junaid Zahoor Dar, and P. Muthu. "Automatic segmentation of kidney stones using active contour method by machine learning." In EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0072521.

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Khalifa, Fahmi, Ahmed Soliman, Ali Takieldeen, et al. "Kidney segmentation from CT images using a 3D NMF-guided active contour model." In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493300.

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Farzaneh, Negar, S. M. Reza Soroushmehr, Hirenkumar Patel, et al. "Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512967.

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Daoud, Mohammad I., Ahmad Shtaiyat, Hadeel A. Younes, Mahasen S. Al-Najar, and Rami Alazrai. "Improved Kidney Outlining in Ultrasound Images by Combining Deep Learning Semantic Segmentation with Conventional Active Contour." In 2023 10th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2023. http://dx.doi.org/10.1109/iceee59925.2023.00021.

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Xie, Xianghua, and Majid Mirmehdi. "Initialisation-Free Active Contour Segmentation." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.567.

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Wang, Kai-bin, Bian-zhang Yu, and Wei Xi. "Radar image segmentation using active contour." In 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar. IEEE, 2007. http://dx.doi.org/10.1109/apsar.2007.4418732.

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Yiming Ma and Peter C. Tay. "Ultrasound despeckling for active contour segmentation." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413890.

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Fan Yang, Frederick W. B. Li, and Rynson W. H. Lau. "Active contour projection for mesh segmentation." In 2009 Joint Conferences on Pervasive Computing (JCPC). IEEE, 2009. http://dx.doi.org/10.1109/jcpc.2009.5420066.

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Lai, Jun, and Ming Ye. "Active Contour Based Lung Field Segmentation." In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2009. http://dx.doi.org/10.1109/ihmsc.2009.80.

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Zia, Hamza, Asim Niaz, and Kwang Nam Choi. "Active Contour Model for Image Segmentation." In 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE). IEEE, 2022. http://dx.doi.org/10.1109/arace56528.2022.00011.

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