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Journal articles on the topic 'Segmentation des images échographiques'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Tourasse, C., A. Coulon, and J. F. Dénier. "Corrélations radio-histologiques des images subtiles échographiques." Journal de Radiologie Diagnostique et Interventionnelle 95, no. 2 (2014): 186–200. http://dx.doi.org/10.1016/j.jradio.2013.12.004.

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2

Dhombres, F., S. Friszer, R. Bessis, and J. M. Jouannic. "Une auto-évaluation simplifiée des images échographiques du premier trimestre." Gynécologie Obstétrique & Fertilité 43, no. 12 (2015): 761–66. http://dx.doi.org/10.1016/j.gyobfe.2015.09.006.

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3

Betrouni, N., M. Vermandel, D. Pasquier, R. Viard, and S. Maouche. "Réduction de speckle et modélisation pour la segmentation d'images échographiques de la prostate." ITBM-RBM 26, no. 4 (2005): 276–78. http://dx.doi.org/10.1016/j.rbmret.2005.06.014.

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4

Zaart. "Skin Images Segmentation." Journal of Computer Science 6, no. 2 (2010): 217–23. http://dx.doi.org/10.3844/jcssp.2010.217.223.

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5

Patel, Punam, and Shamik Tiwari. "Text Segmentation from Images." International Journal of Computer Applications 67, no. 19 (2013): 25–28. http://dx.doi.org/10.5120/11505-7222.

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6

Ahmad, Khairul Adilah, Sharifah Lailee Syed Abdullah, and Mahmod Othman. "Natural Images Contour Segmentation." Journal of Computing Research and Innovation 2, no. 4 (2018): 39–47. http://dx.doi.org/10.24191/jcrinn.v2i4.62.

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This paper, a combination of edge detection and contour based segmentation approach for object contour delineation is proposed. The proposed approach employs a new methodology for segmenting the fruit contour from the indoor and outdoo r natural images more effectively. The overall process is carried out in five steps. The first step is to pre - process the image in order to convert the colour image to grayscale image. Second step is the adoption of Laplacian of Gaussian edge detection and a new corner template detection algorithm for adjustment of the pixels along the edge map in the interpol

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7

Lawand, Komal. "Segmentation of Dermoscopic Images." IOSR Journal of Engineering 4, no. 4 (2014): 16–20. http://dx.doi.org/10.9790/3021-04461620.

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8

VÉHEL, JACQUES LÉVY, and PASCAL MIGNOT. "MULTIFRACTAL SEGMENTATION OF IMAGES." Fractals 02, no. 03 (1994): 371–77. http://dx.doi.org/10.1142/s0218348x94000466.

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We propose a multifractal approach to the problem of image analysis. We show that an alternative description of images, based on a multifractal characterization, can be used instead of the classical approach that involves smoothing of the discrete data in order to compute local extrema. We classify each point of the image according to two parameters, its type of singularity and its relative height, by computing the spectra associated with different kinds of capacities defined from the gray levels. All this information is then used together through a Bayesian approach.

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9

Musatian, S. A., A. V. Lomakin, S. Yu Sartasov, L. K. Popyvanov, I. B. Monakhov, and A. S. Chizhova. "Medical Images Segmentation Operations." Proceedings of the Institute for System Programming of the RAS 30, no. 4 (2018): 183–94. http://dx.doi.org/10.15514/ispras-2018-30(4)-12.

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10

Taxt, T., P. J. Flynn, and A. K. Jain. "Segmentation of document images." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 12 (1989): 1322–29. http://dx.doi.org/10.1109/34.41371.

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11

El Zaart, Ali, Djemel Ziou, Shengrui Wang, and Qingshan Jiang. "Segmentation of SAR images." Pattern Recognition 35, no. 3 (2002): 713–24. http://dx.doi.org/10.1016/s0031-3203(01)00070-x.

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12

Mukherjee, Jayanta, P. P. Das, and B. N. Chatterji. "Segmentation of range images." Pattern Recognition 25, no. 10 (1992): 1141–56. http://dx.doi.org/10.1016/0031-3203(92)90017-d.

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13

Deklerck, R., J. Cornelis, and M. Bister. "Segmentation of medical images." Image and Vision Computing 11, no. 8 (1993): 486–503. http://dx.doi.org/10.1016/0262-8856(93)90068-r.

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14

Lee, J. S., and I. Jurkevich. "Segmentation of SAR images." IEEE Transactions on Geoscience and Remote Sensing 27, no. 6 (1989): 674–80. http://dx.doi.org/10.1109/36.35954.

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15

MOSTOVYI, V., and S. HORIASHCHENKO. "SEGMENTATION OF MEDICAL IMAGES." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 51–56. https://doi.org/10.31891/2307-5732-2020-289-5-51-56.

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Segmentation is an integral part of the digital image processing process. It is the division or division of the image into some parts that meet the specified characteristics and characterize these areas and the image as a whole. At the segmentation stage, issues are solved that complement the standard tasks of image processing, namely coding, restoration, quality improvement. The segmentation process is considered an integral part of the tasks of image recognition, classification and identification. That is why segmentation has found its wide application in such areas as microbiology, medicine

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16

Jiang, Junzhe, Cheng Xu, Hongzhe Liu, Ying Fu, and Muwei Jian. "DSA: Deformable Segmentation Attention for Multi-Scale Fisheye Image Segmentation." Electronics 12, no. 19 (2023): 4059. http://dx.doi.org/10.3390/electronics12194059.

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With a larger field of view (FOV) than ordinary images, fisheye images are becoming mainstream in the field of autonomous driving. However, the severe distortion problem of fisheye images also limits its application. The performance of neural networks designed for narrow FOV images degrades drastically for fisheye images, and the use of large composite models can improve the performance, but it brings huge time overhead and hardware costs. Therefore, we decided to balance real time and accuracy by designing the deformable segmentation attention(DSA) module, a generalpurpose architecture based

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17

Mirzaev, Nomaz, Sobirjon Radjabov, Akhmad Khashimov, Nurilla Noraliev, and Gulmira Mirzaeva. "Image segmentation algorithms." BIO Web of Conferences 138 (2024): 02013. http://dx.doi.org/10.1051/bioconf/202413802013.

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This article focuses on the methods of segmentation of kidney images, with the main emphasis on segmentation methods based on neural networks. During this work, we got acquainted with neural network-based algorithms and decided to use the U-Net algorithm for segmentation. A neural network architecture mainly consists of a descending (left) and an expanding (right) part. The structure of U-Net neural network architectures, which are currently widely used for medical images, have been investigated and experimental studies have been carried out on extracting the kidney region in medical images. T

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18

Kalthom Adam H. Ibrahim, Mohammed Abdallah Almaleeh, Moaawia Mohamed Ahmed, and Dalia Mahmoud Adam. "Images Processing for Segmentation Neisseria Bacteria Cells." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 573–79. http://dx.doi.org/10.30574/wjarr.2021.12.3.0672.

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This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The

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19

Kalthom, Adam H. Ibrahim, Abdallah Almaleeh Mohammed, Mohamed Ahmed Moaawia, and Mahmoud Adam Dalia. "Images Processing for Segmentation Neisseria Bacteria Cells." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 573–79. https://doi.org/10.5281/zenodo.5820372.

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This paper introduces the segmentation of <em>Neisseria </em>bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images

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20

Myasnikov, E. "Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches." Computer Optics 41, no. 4 (2017): 564–72. http://dx.doi.org/10.18287/2412-6179-2017-41-564-572.

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Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known m

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21

Bu, Weifeng, and Mingchuan Zhang. "A Tongue Segmentation Algorithm Based on Deeplabv3+ Network Model." Journal of Computing and Electronic Information Management 10, no. 3 (2023): 46–50. http://dx.doi.org/10.54097/jceim.v10i3.8680.

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When collecting tongue images in an open en- vironment with a mobile portable collection device, there will be problems of different shooting angles and unstable lighting. Due to the strong mobility of the portable acquisition device, the captured images will inevitably be blurred by jitter, which further increases the diﬀiculty of segmentation. This paper applies neural network to tongue images segmentation, and proposes a tongue images segmentation method based on deep convolutional neural network. This method is a tongue images segmentation method based on the semantic segmentation framewor

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22

Liu, Hong, Haijun Wei, Lidui Wei, Jingming Li, and Zhiyuan Yang. "The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm." Advances in Tribology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4931502.

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This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with l

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23

Gowroju, Swathi, V. Swathi, Narsimhulu K., and Shilpa Choudhary. "Semantic Segmentation of Areal Images using Pixel Wise Segmentation." Procedia Computer Science 259 (2025): 463–72. https://doi.org/10.1016/j.procs.2025.03.348.

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24

Botelho, Glenda, Alexandre Tadeu, and Ary Henrique. "Mammographic Images Segmentation using Superpixel." International Journal of Computer Applications 182, no. 11 (2018): 26–30. http://dx.doi.org/10.5120/ijca2018917733.

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25

Noyel, Guillaume, Jesús Angulo, and Dominique Jeulin. "MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES." Image Analysis & Stereology 26, no. 3 (2011): 101. http://dx.doi.org/10.5566/ias.v26.p101-109.

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The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. Data reduction is performed either by Factor Analysis or by model fitting. Image segmentation is done on different spaces: factor space, parameters space, etc. On all these spaces the spatial/spectral se

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26

Wu, H. S., J. Gil, and J. Barba. "Optimal segmentation of cell images." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 1 (1998): 50. http://dx.doi.org/10.1049/ip-vis:19981690.

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27

Lehmann, F. "Turbo Segmentation of Textured Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 1 (2011): 16–29. http://dx.doi.org/10.1109/tpami.2010.58.

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28

Skalski, Andrzej, and Paweł Turcza. "Heart Segmentation in Echo Images." Metrology and Measurement Systems 18, no. 2 (2011): 305–14. http://dx.doi.org/10.2478/v10178-011-0012-y.

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Heart Segmentation in Echo ImagesCardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on

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29

Nikolaeva, O. V. "Segmentation Algorithm of Multispectral Images." Mathematical Models and Computer Simulations 16, no. 3 (2024): 340–51. http://dx.doi.org/10.1134/s2070048224700029.

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30

Xu, L., M. Jackowski, A. Goshtasby, et al. "Segmentation of skin cancer images." Image and Vision Computing 17, no. 1 (1999): 65–74. http://dx.doi.org/10.1016/s0262-8856(98)00091-2.

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31

Levienaise-Obadia, Barbara, and Andrew Gee. "Adaptive segmentation of ultrasound images." Image and Vision Computing 17, no. 8 (1999): 583–88. http://dx.doi.org/10.1016/s0262-8856(98)00177-2.

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32

Sánchez, Claudia, and Mariano Rivera. "Binary Segmentation of Multiband Images." Research in Computing Science 102, no. 1 (2015): 63–75. http://dx.doi.org/10.13053/rcs-102-1-6.

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33

Duarte, A., L. Carrão, M. Espanha, et al. "Segmentation Algorithms for Thermal Images." Procedia Technology 16 (2014): 1560–69. http://dx.doi.org/10.1016/j.protcy.2014.10.178.

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34

Jardim, Sandra M. G. V. B., and Mário A. T. Figueiredo. "Segmentation of fetal ultrasound images." Ultrasound in Medicine & Biology 31, no. 2 (2005): 243–50. http://dx.doi.org/10.1016/j.ultrasmedbio.2004.11.003.

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35

Kundu, Amlan. "Local segmentation of biomedical images." Computerized Medical Imaging and Graphics 14, no. 3 (1990): 173–83. http://dx.doi.org/10.1016/0895-6111(90)90057-i.

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36

Acharya, Raj. "Segmentation of multidimensional cardiac images." Computerized Medical Imaging and Graphics 19, no. 1 (1995): 61–68. http://dx.doi.org/10.1016/0895-6111(94)00044-1.

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37

Park, Jaehyun, and Ludwik Kurz. "Unsupervised segmentation of textured images." Information Sciences 92, no. 1-4 (1996): 255–76. http://dx.doi.org/10.1016/0020-0255(96)00047-3.

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38

Dai, Xiao Yan, and Junji Maeda. "Unsupervised Segmentation of Natural Images." Optical Review 9, no. 5 (2002): 197–201. http://dx.doi.org/10.1007/s10043-002-0197-7.

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39

Farag, A. A., A. S. El-Baz, and G. Gimel'farb. "Precise segmentation of multimodal images." IEEE Transactions on Image Processing 15, no. 4 (2006): 952–68. http://dx.doi.org/10.1109/tip.2005.863949.

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40

Shiming Xiang, Chunhong Pan, Feiping Nie, and Changshui Zhang. "TurboPixel Segmentation Using Eigen-Images." IEEE Transactions on Image Processing 19, no. 11 (2010): 3024–34. http://dx.doi.org/10.1109/tip.2010.2052268.

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41

Zheng, Haiyong, Hongmiao Zhao, Xue Sun, Huihui Gao, and Guangrong Ji. "Automatic setae segmentation fromChaetocerosmicroscopic images." Microscopy Research and Technique 77, no. 9 (2014): 684–90. http://dx.doi.org/10.1002/jemt.22389.

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42

Harika, Dr B., K. Himneesh, and M. Bharath. "Semantic Segmentation For Aerial Images." International Journal of Research Publication and Reviews 6, no. 4 (2025): 1547–63. https://doi.org/10.55248/gengpi.6.0425.1358.

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43

Rossi, Farli, and Ashrani Aizzuddin Abd Rahni. "Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review." International Journal of Engineering & Technology 7, no. 3.32 (2018): 137. http://dx.doi.org/10.14419/ijet.v7i3.32.18414.

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Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation

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44

Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.

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Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get

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45

Li, Hong'an, Man Liu, Jiangwen Fan, and Qingfang Liu. "Biomedical image segmentation algorithm based on dense atrous convolution." Mathematical Biosciences and Engineering 21, no. 3 (2024): 4351–69. http://dx.doi.org/10.3934/mbe.2024192.

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<abstract><p>Biomedical images have complex tissue structures, and there are great differences between images of the same part of different individuals. Although deep learning methods have made some progress in automatic segmentation of biomedical images, the segmentation accuracy is relatively low for biomedical images with significant changes in segmentation targets, and there are also problems of missegmentation and missed segmentation. To address these challenges, we proposed a biomedical image segmentation method based on dense atrous convolution. First, we added a dense atrou

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46

Liang, Yingbo, and Jian Fu. "Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (2019): 1954019. http://dx.doi.org/10.1142/s0218001419540193.

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The traditional watershed algorithm has the limitation of false mark in medical image segmentation, which causes over-segmentation and images to be contaminated by noise possibly during acquisition. In this study, we proposed an improved watershed segmentation algorithm based on morphological processing and total variation model (TV) for medical image segmentation. First of all, morphological gradient preprocessing is performed on MRI images of brain lesions. Secondly, the gradient images are denoised by the all-variational model. While retaining the edge information of MRI images of brain les

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47

Ma, Xiqi, Pengyu Zhang, Xiaofei Man, and Leming Ou. "A New Belt Ore Image Segmentation Method Based on the Convolutional Neural Network and the Image-Processing Technology." Minerals 10, no. 12 (2020): 1115. http://dx.doi.org/10.3390/min10121115.

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In the field of mineral processing, an accurate image segmentation method is crucial for measuring the size distribution of run-of-mine ore on the conveyor belts in real time0The image-based measurement is considered to be real time, on-line, inexpensive, and non-intrusive. In this paper, a new belt ore image segmentation method was proposed based on a convolutional neural network and image processing technology. It consisted of a classification model and two segmentation algorithms. A total of 2880 images were collected as an original dataset from the process control system (PCS). The test im

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48

Sahare, Parul, Jitendra V. Tembhurne, Mayur R. Parate, Tausif Diwan, and Sanjay B. Dhok. "Script-Independent Text Segmentation from Document Images." International Journal of Ambient Computing and Intelligence 13, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijaci.313967.

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Document image analysis finds broad application in the digital world for the purpose of information retrieval. This includes optical character recognition (OCR), indexing of digital libraries, web image processing, etc. One of the important steps in this field is text segmentation. This segmentation becomes complicated for the documents containing text of uneven spacing and characters of varying font sizes. In this paper, script-independent text-line segmentation and word segmentation algorithms are presented. Fast marching method is used for text-line segmentation, whereas wavelet transform w

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49

Abdul, Wadood. "Region Based Segmentation Techniques for Digital Images." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3792–801. http://dx.doi.org/10.1166/jctn.2019.8252.

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This paper discusses region based segmentation techniques for digital images. For a few applications, such as image compression or recognition, we cannot handle the entire picture straightforwardly as it is unconventional and inefficient. Due to these reasons, many algorithms related to image segmentation are proposed in the literature to divide an image prior to compression or recognition. The segmentation of an image is basically done to arrange or group the image in a few fragments (districts) as specified by the elements of an image, for instance, according to the value of pixel or frequen

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50

El-Khatib, S., Y. A. Skobtsov, and S. I. Rodzin. "Hyper heuristic particle swarm optimization method for medical images segmentation." Informatization and communication, no. 2 (February 16, 2021): 22–29. http://dx.doi.org/10.34219/2078-8320-2021-12-2-22-29.

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Purpose. Development of new methods for medical images segmentation, since modern methods of medical diagnostics are largely based on image processing MRI, CT scan etc. Materials and Methods. A hybrid particle swarm algorithm for medical image segmentation is proposed, which includes a modified and elite exponential particle swarm segmentation algorithm in combination with the k-means method. The time complexity of the developed algorithm is investigated on the basis of the drift analysis. It is shown that the developed algorithm for segmentation of MRI images has a polynomial time complexity.

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