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Journal articles on the topic 'Seeded region growing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Adams, R., and L. Bischof. "Seeded region growing." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 6 (June 1994): 641–47. http://dx.doi.org/10.1109/34.295913.

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2

Fan, Minjie, and Thomas C. M. Lee. "Variants of seeded region growing." IET Image Processing 9, no. 6 (June 1, 2015): 478–85. http://dx.doi.org/10.1049/iet-ipr.2014.0490.

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3

Mehnert, Andrew, and Paul Jackway. "An improved seeded region growing algorithm." Pattern Recognition Letters 18, no. 10 (October 1997): 1065–71. http://dx.doi.org/10.1016/s0167-8655(97)00131-1.

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4

Park, Seongjin, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, and Bohyoung Kim. "Parallelized Seeded Region Growing Using CUDA." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/856453.

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This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests.
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5

Park, Sanghyun. "Water Region Segmentation Scheme using Seeded Region Growing." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 8, no. 1 (July 31, 2018): 53–62. http://dx.doi.org/10.14801/jaitc.2018.8.1.53.

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6

Stewart, R. D., I. Fermin, and M. Opper. "Region growing with pulse-coupled neural networks: an alternative to seeded region growing." IEEE Transactions on Neural Networks 13, no. 6 (November 2002): 1557–62. http://dx.doi.org/10.1109/tnn.2002.804229.

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7

Fan, Jianping, Guihua Zeng, Mathurin Body, and Mohand-Said Hacid. "Seeded region growing: an extensive and comparative study." Pattern Recognition Letters 26, no. 8 (June 2005): 1139–56. http://dx.doi.org/10.1016/j.patrec.2004.10.010.

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8

Shih, Frank Y., and Shouxian Cheng. "Automatic seeded region growing for color image segmentation." Image and Vision Computing 23, no. 10 (September 2005): 877–86. http://dx.doi.org/10.1016/j.imavis.2005.05.015.

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9

Li, Qianwen, Zhihua Wei, and Cairong Zhao. "Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction." International Journal of Image and Graphics 17, no. 04 (October 2017): 1750024. http://dx.doi.org/10.1142/s0219467817500243.

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Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.
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10

Vijayalakshmi, S., and Savita. "An Enhanced Seeded Region Growing Based Techniques for Hippocampus Segmentation." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2308–20. http://dx.doi.org/10.1166/jctn.2020.8889.

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In to this new era of technology image processing plays a very important role in medical where in many applications analysis of images is used. In medical image processing image segmentation is very important task in which image if partitioned in to disjoint meaningful regions or parts. Lots of image segmentation techniques are used which are different from each other in a way of working. In this our main area of focus is Hippocampus which is a part of the brain and helps in formation of new and long term memory. Alzheimer is a memory related brain disease in which the volume of hippocampus shrink day by day. Many researches has been done in this area so in this paper we explained the works done by many researcher and also present a HC segmentation method based on seeded region growing.
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11

Feng, Qizhi, Bin Gao, Peng Lu, W. L. Woo, Yang Yang, Yunchen Fan, Xueshi Qiu, and Liangyong Gu. "Automatic seeded region growing for thermography debonding detection of CFRP." NDT & E International 99 (October 2018): 36–49. http://dx.doi.org/10.1016/j.ndteint.2018.06.001.

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12

ChuinMu Wang, and RueyMaw Chen. "Vector Seeded Region Growing for Parenchyma Classification in Brain MRI." International Journal of Advancements in Computing Technology 3, no. 2 (March 31, 2011): 49–56. http://dx.doi.org/10.4156/ijact.vol3.issue2.7.

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13

Li, Liang, Xiaoling Zhang, Ling Pu, Liming Pu, Bokun Tian, Liming Zhou, and Shunjun Wei. "3D SAR Image Background Separation Based on Seeded Region Growing." IEEE Access 7 (2019): 179842–63. http://dx.doi.org/10.1109/access.2019.2955296.

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14

Pan, Jun, Zhonghao Fang, Shengtong Chen, Huan Ge, Fen Hu, and Mi Wang. "An Improved Seeded Region Growing-Based Seamline Network Generation Method." Remote Sensing 10, no. 7 (July 5, 2018): 1065. http://dx.doi.org/10.3390/rs10071065.

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15

Kang, Chung-Chia, Wen-June Wang, and Chung-Hao Kang. "Image segmentation with complicated background by using seeded region growing." AEU - International Journal of Electronics and Communications 66, no. 9 (September 2012): 767–71. http://dx.doi.org/10.1016/j.aeue.2012.01.011.

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16

Garyanova, E. D., A. S. Sokolov, S. V. Gulyaeva, and E. V. Polyakova. "GROWING OF FIELD-SEEDED AUBERGINE IN THE LOWER VOLGA REGION." Bulletin of KSAU, no. 10 (2020): 13–20. http://dx.doi.org/10.36718/1819-4036-2020-10-13-20.

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17

Chang, Yen Che, Kuei Ting Kuo, Zih Yi Wang, and Chuin Mu Wang. "Seeded Region Growing Based on Extension for Multispectral MR Images Classification." Advanced Materials Research 1079-1080 (December 2014): 872–77. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.872.

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In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.
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18

Wu, Jie, Skip Poehlman, Michael D. Noseworthy, and Markad V. Kamath. "Texture feature based automated seeded region growing in abdominal MRI segmentation." Journal of Biomedical Science and Engineering 02, no. 01 (2009): 1–8. http://dx.doi.org/10.4236/jbise.2009.21001.

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19

Sajadi, AtefehSadat, and SeyedHojat Sabzpoushan. "A New Seeded Region Growing Technique for Retinal Blood Vessels Extraction." Journal of Medical Signals & Sensors 4, no. 3 (2014): 223. http://dx.doi.org/10.4103/2228-7477.137841.

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20

Zhao, Wangda, Weixiang Chen, Yujie Liu, Xiangwei Wang, and Yang Zhou. "A smoke segmentation algorithm based on improved intelligent seeded region growing." Fire and Materials 43, no. 6 (June 4, 2019): 725–33. http://dx.doi.org/10.1002/fam.2724.

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21

Avazpour, Iman, M. Iqbal Saripan, Abdul Jalil Nordin, and Raja Syamsul Azmir Raja Abdullah. "Segmentation of Extrapulmonary Tuberculosis Infection Using Modified Automatic Seeded Region Growing." Biological Procedures Online 11, no. 1 (July 14, 2009): 241–52. http://dx.doi.org/10.1007/s12575-009-9013-0.

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22

Saputra, Wanvy Arifha, and Agus Zainal Arifin. "Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna." JURNAL INFOTEL 9, no. 1 (February 4, 2017): 56. http://dx.doi.org/10.20895/infotel.v9i1.164.

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The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.
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23

Mallikarjunaswamy, M. S., Mallikarjun S. Holi, and Rajesh Raman. "Knee Joint Menisci Segmentation, Visualization and Quantification Using Seeded Region Growing Algorithm." Journal of Medical Imaging and Health Informatics 5, no. 3 (June 1, 2015): 552–60. http://dx.doi.org/10.1166/jmihi.2015.1435.

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24

Pan, Jun, and Mi Wang. "Improved seeded region growing for detection of water bodies in aerial images." Geo-spatial Information Science 19, no. 1 (January 2, 2016): 1–8. http://dx.doi.org/10.1080/10095020.2015.1127628.

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25

Jianping Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref. "Automatic image segmentation by integrating color-edge extraction and seeded region growing." IEEE Transactions on Image Processing 10, no. 10 (2001): 1454–66. http://dx.doi.org/10.1109/83.951532.

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26

Dehdasht-Heydari, Ramin, and Sadegh Gholami. "Automatic Seeded Region Growing (ASRG) Using Genetic Algorithm for Brain MRI Segmentation." Wireless Personal Communications 109, no. 2 (May 28, 2019): 897–908. http://dx.doi.org/10.1007/s11277-019-06596-4.

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27

Shrivastava, Neeraj, and Jyoti Bharti. "Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review." International Journal of Image and Graphics 20, no. 03 (July 2020): 2050018. http://dx.doi.org/10.1142/s0219467820500187.

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In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.
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28

Grinias, I., and G. Tziritas. "A semi-automatic seeded region growing algorithm for video object localization and tracking." Signal Processing: Image Communication 16, no. 10 (August 2001): 977–86. http://dx.doi.org/10.1016/s0923-5965(01)00014-5.

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29

Lin, Geng-Cheng, Wen-June Wang, Chung-Chia Kang, and Chuin-Mu Wang. "Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing." Magnetic Resonance Imaging 30, no. 2 (February 2012): 230–46. http://dx.doi.org/10.1016/j.mri.2011.09.008.

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30

Yeom, Junho, Minyoung Jung, and Yongil Kim. "Detecting damaged building parts in earthquake-damaged areas using differential seeded region growing." International Journal of Remote Sensing 38, no. 4 (January 13, 2017): 985–1005. http://dx.doi.org/10.1080/01431161.2016.1274445.

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31

He, Yuan. "Automatic seeded region growing based on gradient vector flow for color image segmentation." Optical Engineering 46, no. 4 (April 1, 2007): 047003. http://dx.doi.org/10.1117/1.2724876.

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32

Gao, Haiming, Xuebo Zhang, Yongchun Fang, and Jing Yuan. "A line segment extraction algorithm using laser data based on seeded region growing." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141875524. http://dx.doi.org/10.1177/1729881418755245.

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This article presents a novel line segment extraction algorithm using two-dimensional (2D) laser data, which is composed of four main procedures: seed-segment detection, region growing, overlap region processing, and endpoint generation. Different from existing approaches, the proposed algorithm borrows the idea of seeded region growing in the field of image processing, which is more efficient with more precise endpoints of the extracted line segments. Comparative experimental results with respect to the well-known Split-and-Merge algorithm are presented to show superior performance of the proposed approach in terms of efficiency, correctness, and precision, using real 2D data taken from our hallway and laboratory.
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33

Somasundaram, K., and P. Kalavathi. "Brain segmentation in magnetic resonance human head scans using multi-seeded region growing." Imaging Science Journal 62, no. 5 (December 19, 2013): 273–84. http://dx.doi.org/10.1179/1743131x13y.0000000068.

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34

Sanchez Hernandez, Javier, Estibaliz Martinez Izquierdo, and Agueda Arquero Hidalgo. "Improving Parameters Selection of a Seeded Region Growing Method for Multiband Image Segmentation." IEEE Latin America Transactions 13, no. 3 (March 2015): 843–49. http://dx.doi.org/10.1109/tla.2015.7069113.

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35

SrinivasBhargav, Chirumamilla, and Kamlesh Murari. "Adapting Seeded Region Growing for Segmenting the Flooded Area from the SAR Images." International Journal of Engineering Trends and Technology 9, no. 6 (March 25, 2014): 277–81. http://dx.doi.org/10.14445/22315381/ijett-v9p255.

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36

Panda, Rashmi, N. B. Puhan, and Ganapati Panda. "New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation." Biocybernetics and Biomedical Engineering 36, no. 1 (2016): 119–29. http://dx.doi.org/10.1016/j.bbe.2015.10.005.

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37

Kaur, Harsimranjot, and Dr Reecha Sharma. "Segmentation of Tumor Region from Brain Mri Images Using Fuzzy C-Means Clustering And Seeded Region Growing." IOSR Journal of Computer Engineering 18, no. 05 (May 2016): 20–24. http://dx.doi.org/10.9790/0661-1805012024.

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38

Hore, Sirshendu, Souvik Chakraborty, Sankhadeep Chatterjee, Nilanjan Dey, Amira S. Ashour, Le Van Chung, and Dac-Nhuong Le. "An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2773. http://dx.doi.org/10.11591/ijece.v6i6.11801.

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<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>
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39

Hore, Sirshendu, Souvik Chakraborty, Sankhadeep Chatterjee, Nilanjan Dey, Amira S. Ashour, Le Van Chung, and Dac-Nhuong Le. "An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2773. http://dx.doi.org/10.11591/ijece.v6i6.pp2773-2780.

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<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>
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40

XU Han. "Research on Remote Sensing Image Segmentation Technology Based on Improved Seeded region growing method." International Journal of Digital Content Technology and its Applications 7, no. 8 (April 30, 2013): 371–79. http://dx.doi.org/10.4156/jdcta.vol7.issue8.40.

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41

Herumurti, Darlis, Keiichi Uchimura, Gou Koutaki, and Takumi Uemura. "Automatic Road Extraction Using Seeded Region Growing with Mixed ART Method for DSM Data." IEEJ Transactions on Electronics, Information and Systems 133, no. 1 (2013): 159–68. http://dx.doi.org/10.1541/ieejeiss.133.159.

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42

Ramli, Muhamad Farid, and Khairul Nizam Tahar. "Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation." Geo-spatial Information Science 23, no. 3 (July 2, 2020): 195–208. http://dx.doi.org/10.1080/10095020.2020.1805366.

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43

Zhou, Jianhua, Yan Huang, and Bailang Yu. "Mapping Vegetation-Covered Urban Surfaces Using Seeded Region Growing in Visible-NIR Air Photos." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 5 (May 2015): 2212–21. http://dx.doi.org/10.1109/jstars.2014.2362308.

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44

Byun, Y., D. Kim, J. Lee, and Y. Kim. "A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging." International Journal of Remote Sensing 32, no. 16 (August 20, 2011): 4589–609. http://dx.doi.org/10.1080/01431161.2010.489066.

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45

Wang, Chuin-Mu, and Geng-Cheng Lin. "A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/290607.

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After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST andK-means.
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46

Allahverdiyev, Elkhan Rajab, and Aytekin Sabir Mamedova. "THE GROWING TECHNOLOGY OF MIXED SEEDED PLANTS AT STUBBLE-FIELD." Agrarian Scientific Journal, no. 8 (September 10, 2021): 57–61. http://dx.doi.org/10.28983/asj.y2021i8pp57-61.

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One of the main measures to increase production in the field of grain and feed production in our republic is to increase the yield of grain and forage crops on plantations.The purpose of our research work is to study the effect of fertilization and irrigation rates on the yield, crop quality and soil fertility of crops sown in a mixture when planting honeysuckle. The results of research conducted in world agriculture show that mainly the yield and composition of mixed crops, the quality indicators of which depend on the components of the crops to be sown, their seeding rate and cultivation technology. As a result of studies conducted in the conditions of ancient irrigated gray-meadow soils of the Karabakh region of our republic, it was found that with 4 - fold vegetation irrigation (4200 m3), the yield of mixed sowing in the control version without fertilizers was 372 c/ha, while under the influence of mineral and organic mineral fertilizers, the yield increased and amounted to 447-627 c/ha. Based on the results of the study, it can be said that in order to obtain a high yield of green mass with the joint sowing of corn and soybeans, the optimal irrigation and fertilizer application rates were determined, the highest indicator was achieved in the variant where the fertilizer standards N120P150C150 were applied. In the course of the study, the influence of fertilizer application rates and optimization of the amount of irrigation on the quality indicators of feed, as well as on the yield obtained from mixed crops, was studied. As a result of the conducted analyses, the amount of raw protein, absolute dry matter, nitrate nitrogen in the natural mass, the yield of feed units per hectare and the amount of protein going for digestion were determined. Thus, the application of fertilizers within the optimal limits and the correct, timely supply of irrigation standards significantly increase the quality of the crop on a par with the yield on mixed crops, the soil fertility is preserved.
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47

Zhou, Hong, Hai-er Xu, Pei-qi He, Zhi-bai Song, and Chen-ge Geng. "Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm." Journal of Zhejiang University SCIENCE C 11, no. 3 (February 7, 2010): 199–205. http://dx.doi.org/10.1631/jzus.c0910144.

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48

Al-Faris, Ali Qusay, Umi Kalthum Ngah, Nor Ashidi Mat Isa, and Ibrahim Lutfi Shuaib. "Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)." Journal of Digital Imaging 27, no. 1 (October 8, 2013): 133–44. http://dx.doi.org/10.1007/s10278-013-9640-5.

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49

Yamazaki, Y., K. Murase, Y. Tuduki, Y. Nishimura, T. Kitada, M. Shinohara, S. Iwamoto, et al. "Development of automated seeded region growing algorithm for extraction of cerebral blood vessels from magnetic resonance angiography." International Congress Series 1230 (June 2001): 1162–63. http://dx.doi.org/10.1016/s0531-5131(01)00211-4.

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50

., Tejus Thirumeni. "3D SEGMENTATION OF GLIOMA FROM BRAIN MR IMAGES USING SEEDED REGION GROWING AND FUZZY C-MEANS CLUSTERING." International Journal of Research in Engineering and Technology 04, no. 24 (October 25, 2015): 79–83. http://dx.doi.org/10.15623/ijret.2015.0424014.

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