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Journal articles on the topic 'Segmentation des images échographiques'

Author: Grafiati

Published: 25 May 2024

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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 (February 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 (December 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 (September 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 (February 1, 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 (April 18, 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 (January 30, 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 interpolation process. Third step is the reconstruction process by implementing two morphology operators with embedded of inversion condition and dynamic thr eshold to preserve and reconstruct object contour. Fifth step is ground mask process in which the outputs of the inference obtained for each pixel is combined to a final segmented output, which provides a segmented foreground against the black background. This proposed algorithm is tested over 150 indoor and 40 outdoor fruit images in order to analyse its efficiency. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy of 100 % in segmenting indoor and outdoor natural images. This algorithm also present a fully automatic model based system for segmenting fruit images of the natural environment.

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Lawand, Komal. "Segmentation of Dermoscopic Images." IOSR Journal of Engineering 4, no. 4 (April 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 (September 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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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 (December 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 (March 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 (October 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 (October 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

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 (September 27, 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 on a deformable attention mechanism and a spatial pyramid architecture. The deformable mechanism serves to accurately extract feature information from fisheye images, together with attention to learn the global context and the spatial pyramid structure to balance multiscale feature information, thus improving the perception of fisheye images by traditional networks without increasing the amount of excessive computation. Lightweight networks such as SegNeXt equipped with the DSA module enable effective and rapid multi-scale segmentation of fisheye images in complex scenes. Our architecture achieves outstanding results on the WoodScape dataset, while our ablation experiments demonstrate the effectiveness of various parts of the architecture.

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16

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 measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images.

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17

Botelho, Glenda, Alexandre Tadeu, and Ary Henrique. "Mammographic Images Segmentation using Superpixel." International Journal of Computer Applications 182, no. 11 (August 14, 2018): 26–30. http://dx.doi.org/10.5120/ijca2018917733.

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18

Noyel, Guillaume, Jesús Angulo, and Dominique Jeulin. "MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES." Image Analysis & Stereology 26, no. 3 (May 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 segmentation approach is applied, leading to relevant results on the image.

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19

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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20

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

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21

Skalski, Andrzej, and Paweł Turcza. "Heart Segmentation in Echo Images." Metrology and Measurement Systems 18, no. 2 (January 1, 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 the recently introduced variant of the level set method called level set without edges. This variant takes advantage of the intensity value of area information instead of module of gradient which is typically used. Such approach guarantees stability and correctness of algorithm working on the border between object and background with small absolute value of image gradient. To reassure meaningful results, the image segmentation is proceeded with automatic Region of Interest (ROI) calculation. The main idea of ROI calculations is to receive a triangle-like part of the acquired ECHO image, using linear Hough transform, thresholding and simple mathematics. Additionally, in order to improve the images quality, an anisotropic diffusion filter, before ROI calculation, was used. The proposed method has been tested on real echocardiographic image sequences. Derived results confirm the effectiveness of the presented method.

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22

Xu, L., M. Jackowski, A. Goshtasby, D. Roseman, S. Bines, C. Yu, A. Dhawan, and A. Huntley. "Segmentation of skin cancer images." Image and Vision Computing 17, no. 1 (January 1999): 65–74. http://dx.doi.org/10.1016/s0262-8856(98)00091-2.

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23

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

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24

Sánchez, Claudia, and Mariano Rivera. "Binary Segmentation of Multiband Images." Research in Computing Science 102, no. 1 (December 31, 2015): 63–75. http://dx.doi.org/10.13053/rcs-102-1-6.

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25

Duarte, A., L. Carrão, M. Espanha, T. Viana, D. Freitas, P. Bártolo, P. Faria, and H. A. Almeida. "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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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

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

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33

Zheng, Haiyong, Hongmiao Zhao, Xue Sun, Huihui Gao, and Guangrong Ji. "Automatic setae segmentation fromChaetocerosmicroscopic images." Microscopy Research and Technique 77, no. 9 (June 10, 2014): 684–90. http://dx.doi.org/10.1002/jemt.22389.

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34

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 little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.

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35

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 (December 30, 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 images segmentation represent significant step in extracting images features and diagnoses the disease by computer software applications.

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36

Bu, Weifeng, and Mingchuan Zhang. "A Tongue Segmentation Algorithm Based on Deeplabv3+ Network Model." Journal of Computing and Electronic Information Management 10, no. 3 (May 24, 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 framework of DeeplabV3+. First, we modify the output category of the network. Because only the tongue region is segmented, segmentation targets can be divided into two categories when performing tongue images segmentation. One is the tongue region and the other is the background region. Then we replace the backbone network of DeeplabV3+ with a lightweight network and add an attention mechanism. Finally, we use the collected tongue images in the open environment to train the network. After the network obtains the initial segmentation result, tongue images are restored according to the same type of label, so as to obtain the required tongue images only containing tongues. The experimental results show that the method has higher segmentation accuracy for tongue images in open environment, and can better meet the needs of people for tongue images segmentation.

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37

Tatyankin, Vitaly M., and Irina S. Dyubko. "Image segmentation." Yugra State University Bulletin 11, no. 2 (June 15, 2015): 99–101. http://dx.doi.org/10.17816/byusu201511299-101.

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38

Bhunia, Bratasee. "Region Growing Segmentation for Brain Tumor Segmentation-Based MRI Images." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2725–28. http://dx.doi.org/10.22214/ijraset.2023.54121.

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Abstract: Techniques for brain imaging are crucial in identifying the causes of brain cell damage. Therefore, earlier detection of these disorders can have a significant impact on treatment options and help to prevent any potential problems for the patient. One of the main reasons for fatalities in humans is brain tumours. It is obvious that early detection and proper classification of the tumour can increase the likelihood of survival. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researchers have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network(CNN).The goal of this paper is to discuss conventional methods of brain tumor segmentation with focus on region-growing segmentation from MRI images. For the implementation of the work brain tumor image from Kaggle and Image Processing Toolbox under Matlab Software have been used.

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39

Nair, Anitha T., Anitha M. L., and Arun Kumar M. N. "Segmentation of Retinal Images Using Improved Segmentation Network, MesU-Net." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 15 (October 25, 2023): 77–91. http://dx.doi.org/10.3991/ijoe.v19i15.41969.

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Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future.

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40

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 (January 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 with connected components (CCs) labeling is used for word segmentation. Fast marching method is used as a region growing process that detects potential text-lines. For word segmentation, energy map is calculated using wavelet transform to create text-blocks. Both the proposed algorithms are evaluated on different databases containing documents of different scripts, where highest text-line and word segmentation accuracies of 98.9% and 99.1%, respectively, are obtained.

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41

Abdul, Wadood. "Region Based Segmentation Techniques for Digital Images." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 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 frequency response. Currently, many image segmentation approaches exist and are widely used in across scientific disciplines and daily human life. The segmentation approaches can be generally categorized to segmentation based on region, segmentation based on edges, and information grouping.

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42

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 (August 26, 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 of tumor in PET - CT images is a well-known puzzle task. This paper attempted to describe and review four innovative methods used in the joint segmentation of functional and anatomical PET - CT images for tumor delineation. For the basic knowledge, the state of the art image segmentation methods were briefly reviewed and fundamental of PET and CT images were briefly explained. Further, the specific characteristics and limitations of four joint segmentation methods were critically discussed.

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43

Li, Yutong. "The Application of Semantic Segmentation on 2D images." Highlights in Science, Engineering and Technology 31 (February 10, 2023): 88–96. http://dx.doi.org/10.54097/hset.v31i.4818.

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A fundamental problem in computer vision is semantic segmentation, which calls for the algorithm to categorize each pixel in the picture and provide the precise details of the category. Semantic segmentation is being employed extensively in a variety of applications, including autonomous vehicles and medical imaging. An overview of similar semantic segmentation approaches is given in this study. First, this paper gives a brief overview of the history and vocabulary of semantic segmentation. The key datasets for semantic segmentation, conventional segmentation models, and fundamental deep learning techniques for semantic segmentation will then be covered. In particular, traditional methods centered on context models and deep learning-centered methods are discussed in detail. Finally, we review several assessment techniques, including their benefits and drawbacks, and outline the key issues facing semantic segmentation today. In addition, this study seeks to provide an overview of the relevant literature and the difficulties in semantic segmentation. Finally, the paper summarizes the semantic segmentation and prospects the future.

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44

Oh, Ju-Young, and Jung-Min Park. "Interactive Part Segmentation Using Edge Images." Applied Sciences 11, no. 21 (October 28, 2021): 10106. http://dx.doi.org/10.3390/app112110106.

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As more and more fields utilize deep learning, there is an increasing demand to make suitable training data for each field. The existing interactive object segmentation models can easily make the mask label data because these can accurately segment the area of the target object through user interaction. However, it is difficult to accurately segment the target part in the object using the existing models. We propose a method to increase the accuracy of part segmentation by using the proposed interactive object segmentation model trained only with edge images instead of color images. The results evaluated with the PASCAL VOC Part dataset show that the proposed method can accurately segment the target part compared to the existing interactive object segmentation model and the semantic part-segmentation model.

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45

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 the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.

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46

Sun, Zengguo, Hui Geng, Zheng Lu, Rafał Scherer, and Marcin Woźniak. "Review of Road Segmentation for SAR Images." Remote Sensing 13, no. 5 (March 7, 2021): 1011. http://dx.doi.org/10.3390/rs13051011.

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Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.

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Song, Yinglei, Benjamin Adobah, Junfeng Qu, and Chunmei Liu. "Segmentation of Ordinary Images and Medical Images with an Adaptive Hidden Markov Model and Viterbi Algorithm." Current Signal Transduction Therapy 15, no. 2 (December 1, 2020): 109–23. http://dx.doi.org/10.2174/1574362413666181109113834.

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Background: Image segmentation is an important problem in both image processing and computer vision. Given an image, the goal of image segmentation is to label each pixel in the image such that the pixels with the same label collectively represent an object. Materials and Methods: Due to the inherent complexity and noise that may exist in images, developing an algorithm that can generate excellent segmentation results for an arbitrary image is still a challenging problem. In this paper, a new adaptive Hidden Markov Model is developed to describe the spatial and semantic relationships among pixels in an image. Based on this statistical model, image segmentation can be efficiently performed with an adaptive Viterbi algorithm in linear time. Results: The algorithm is unsupervised and does not require being used along with any other approach in image segmentation. Testing results on synthetic and real images show that this algorithm is able to achieve excellent segmentation results in both ordinary images and medical images. Conclusion: An implementation of this algorithm in MATLAB is freely available upon request.

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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 atrous convolution module (DAC) between the encoding and decoding paths of the U-Net network. This module was based on the inception structure and atrous convolution design, which can effectively capture multi-scale features of images. Second, we introduced a dense residual pooling module to detect multi-scale features in images by connecting residual pooling blocks of different sizes. Finally, in the decoding part of the network, we adopted an attention mechanism to suppress background interference by enhancing the weight of the target area. These modules work together to improve the accuracy and robustness of biomedical image segmentation. The experimental results showed that compared to mainstream segmentation networks, our segmentation model exhibited stronger segmentation ability when processing biomedical images with multiple-shaped targets. At the same time, this model can significantly reduce the phenomenon of missed segmentation and missegmentation, improve segmentation accuracy, and make the segmentation results closer to the real situation.</p></abstract>

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Yi, Jingru, Hui Tang, Pengxiang Wu, Bo Liu, Daniel J. Hoeppner, Dimitris N. Metaxas, Lianyi Han, and Wei Fan. "Object-Guided Instance Segmentation for Biological Images." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12677–84. http://dx.doi.org/10.1609/aaai.v34i07.6960.

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Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.

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

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

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