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Relevant bibliographies by topics / Semi-automatic segmentation algorithm

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Academic literature on the topic 'Semi-automatic segmentation algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Semi-automatic segmentation algorithm"

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Kim, Sewon, Won Bae, Koichi Masuda, Christine Chung, and Dosik Hwang. "Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines." Applied Sciences 8, no. 9 (2018): 1586. http://dx.doi.org/10.3390/app8091586.

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We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we develo

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Haniff, Nurin Syazwina Mohd, Muhammad Khalis Abdul Karim, Nurul Huda Osman, M. Iqbal Saripan, Iza Nurzawani Che Isa, and Mohammad Johari Ibahim. "Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)." Diagnostics 11, no. 9 (2021): 1573. http://dx.doi.org/10.3390/diagnostics11091573.

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Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR

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Duan, Ye, Xiaoling Li, and Yongjian Xi. "Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging." International Journal of Biomedical Imaging 2007 (2007): 1–5. http://dx.doi.org/10.1155/2007/90216.

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We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally.

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Nikesh, P., and G. Raju. "Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation." International Journal of Image and Graphics 20, no. 03 (2020): 2050021. http://dx.doi.org/10.1142/s0219467820500217.

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Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed appr

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Gorbatsevich, V., B. Kulgildin, M. Melnichenko, O. Vygolov, and Y. Vizilter. "SEMI-AUTOMATIC CITYSCAPE 3D MODEL RESTORATION USING GENERATIVE ADVERSARIAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 415–20. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-415-2020.

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Abstract. The paper addresses the problem of a city heightmap restoration using satellite view image and some manually created area with 3D data. We propose the approach based on generative adversarial networks. Our algorithm contains three steps: low quality 3D restoration, buildings segmentation using restored model, and high-quality 3D restoration. CNN architecture based on original ResDilation blocks and ResNet is used for steps one and three. Training and test datasets were retrieved from National Lidar Dataset (United States) and the algorithm achieved approximately MSE = 3.84 m on this

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Wei, Yun Tao, and Yi Bing Zhou. "Research on CT Image Segmentation of Computer-Aided Liver Operation." Applied Mechanics and Materials 513-517 (February 2014): 3115–21. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3115.

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The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative s

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Rodríguez, Joaquín, Gilberto Ochoa-Ruiz, and Christian Mata. "A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow." Applied Sciences 10, no. 18 (2020): 6163. http://dx.doi.org/10.3390/app10186163.

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Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling th

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Song, Liu, Ling, and Yue. "Automatic Semi-Global Artificial Shoreline Subpixel Localization Algorithm for Landsat Imagery." Remote Sensing 11, no. 15 (2019): 1779. http://dx.doi.org/10.3390/rs11151779.

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Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentatio

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Qi, Yin Cheng, Ting Li, Ming Xiao Xi, Zhen Bing Zhao, and Yin Ping Cai. "An Image Positioning Method of Automatic Random Walker Based on IFS Edge Detection." Applied Mechanics and Materials 599-601 (August 2014): 802–6. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.802.

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The segmentation method of classical random walker needs to select seed points artificially, and may cause incomplete or inaccurate segmentation. In view of these problems, this paper presents an image positioning method of automatic random walker based on IFS (Intuitionistic Fuzzy Set). IFS edge detection method is used to get the edge information of image, and then the connected domains are found out in image edge using the morphology method. Select the central pixels of each connected domain as seed points for automatic random walker, and then segment the IFS edge image by random walker. Ex

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Sulayman, Nisreen, Moustafa Al-Mawaldi, and Qosai Kanafani. "Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images." Egyptian Journal of Radiology and Nuclear Medicine 47, no. 3 (2016): 859–65. http://dx.doi.org/10.1016/j.ejrnm.2016.03.016.

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Dissertations / Theses on the topic "Semi-automatic segmentation algorithm"

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Ghadyani, Hamid R. "Semi Automatic Segmentation of a Rat Brain Atlas." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050305-140247/.

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Chiu, Bernard. "A new segmentation algorithm for prostate boundary detection in 2D ultrasound images." Thesis, Waterloo, Ont. : University of Waterloo, [Dept. of Electrical and Computer Engineering], 2003. http://etd.uwaterloo.ca/etd/bcychiu2003.pdf.

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Thesis (M.Sc.)--University of Waterloo, 2003.<br>"A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering". Includes bibliographical references.

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Zlateski, Aleksandar. "Scalable algorithms for semi-automatic segmentation of electron microscopy images of the brain tissue." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105955.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 139-145).<br>I present a set of fast and scalable algorithms for segmenting very large 3D images of brain tissue. Currently, light and electron microscopy can now produce terascale 3D images within hours. Extracting the infor

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Kéchichian, Razmig. "Structural priors for multiobject semi-automatic segmentation of three-dimensional medical images via clustering and graph cut algorithms." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00967381.

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We develop a generic Graph Cut-based semiautomatic multiobject image segmentation method principally for use in routine medical applications ranging from tasks involving few objects in 2D images to fairly complex near whole-body 3D image segmentation. The flexible formulation of the method allows its straightforward adaption to a given application.\linebreak In particular, the graph-based vicinity prior model we propose, defined as shortest-path pairwise constraints on the object adjacency graph, can be easily reformulated to account for the spatial relationships between objects in a given pro

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Book chapters on the topic "Semi-automatic segmentation algorithm"

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Matthies, Philipp, Jan Wuestemann, Francisco A. Pinto, and Julius C. Neba. "First Validation of Semi-automatic Liver Segmentation Algorithm." In IFMBE Proceedings. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9035-6_50.

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Nielsen, Casper F., and Peter J. Passmore. "Towards a Robust Path Growing Algorithm for Semi-automatic MRI Segmentation." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_237.

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Roxo, Diogo, José Silvestre Silva, Jaime B. Santos, Paula Martins, Eduardo Castela, and Rui Martins. "Cardiac Chamber Contour Extraction." In Information Systems and Technologies for Enhancing Health and Social Care. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3667-5.ch018.

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Segmentation of echocardiography images presents a great challenge since such images contain strong speckle noise and artifacts. Most ultrasound segmentation methods are semi-automatic, requiring initial contour to be manually identified in the images. In this chapter, a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function is used to extract simultaneously the four heart cavities in a fully automatic way. The idea is to evaluate the algorithm potential for the clinical practice as an additional tool helping the physician´s decision. Thus, the extracted contours are compared with the ones sketched by four physicians using for that several metrics, namely distance error, maximum distance, pratt function, similarity angle, similarity region, hausdorff distance, accuracy, overlap, sensitivity, and specificity. The authors show that the proposed algorithm performs well, producing contours very similar to the physicians’ ones. The experimental work was based on echocardiography images of children.

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Conference papers on the topic "Semi-automatic segmentation algorithm"

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Zhang, Xiaoli, Xiongfei Li, Hongpeng Li, and Yuncong Feng. "A semi-automatic brain tumor segmentation algorithm." In 2016 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2016. http://dx.doi.org/10.1109/icme.2016.7553003.

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Shin, Jong-Han, Daehee Kim, and Yo-Sung Ho. "Semi-automatic video segmentation algorithm using virtual blue screens." In Electronic Imaging 2002, edited by C. C. Jay Kuo. SPIE, 2002. http://dx.doi.org/10.1117/12.453099.

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Shum, Judy, Adam Goldhammer, Elena DiMartino, and Ender Finol. "CT Imaging of Abdominal Aortic Aneurysms: Semi-Automatic Vessel Wall Detection and Quantification of Wall Thickness." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192638.

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Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may provide useful information to predict rupture risk. Our procedure for estimating wall thickness in AAAs includes medical image segmentation and wall thickness detection. Image segmentation requires identifying and segmenting the luminal and outer wall boundaries of the blood vessels and wall thickness can be calculated by using intensity histograms and neural networks. The goal of this study is to develop an image-based, semi-automated method to trace the contours of the vessel wall and measure the wall

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Karsch, Kevin, Qing He, and Ye Duan. "A Fast, Semi-automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.40.

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Ganesan, P., and V. Rajini. "Value based semi automatic segmentation of satellite images using HSV color space, histogram equalization and modified FCM clustering algorithm." In 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE). IEEE, 2013. http://dx.doi.org/10.1109/icgce.2013.6823403.

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Mishra, Akshaya, Sepideh Hariri, Alireza A. Moayed, Kostadinka Bizheva, Paul Fieguth, and David Clausi. "A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh resolution optical coherence images of rat retina." In 2010 IEEE 23rd Canadian Conference on Electrical and Computer Engineering - CCECE. IEEE, 2010. http://dx.doi.org/10.1109/ccece.2010.5575182.

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Ballangan, C., Chung Chan, Xiuying Wang, and D. D. Feng. "The impact of reconstruction algorithms on semi-automatic small lesion segmentation for PET: A phantom study." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6092093.

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