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Artículos de revistas sobre el tema "Classification/segmentation"

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Publicado: 1 de junio de 2024

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1

Levner, Ilya, and Hong Zhang. "Classification-Driven Watershed Segmentation." IEEE Transactions on Image Processing 16, no. 5 (2007): 1437–45. http://dx.doi.org/10.1109/tip.2007.894239.

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2

Pavlidis, Theo, and Jiangying Zhou. "Page segmentation and classification." CVGIP: Graphical Models and Image Processing 54, no. 6 (1992): 484–96. http://dx.doi.org/10.1016/1049-9652(92)90068-9.

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3

Khanykov, I. G. "Classification of image segmentation algorithms." Izvestiâ vysših učebnyh zavedenij. Priborostroenie 61, no. 11 (2018): 978–87. http://dx.doi.org/10.17586/0021-3454-2018-61-11-978-987.

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4

Wang, Ying, Jie Su, Qiuyu Xu, and Yixin Zhong. "A Collaborative Learning Model for Skin Lesion Segmentation and Classification." Diagnostics 13, no. 5 (2023): 912. http://dx.doi.org/10.3390/diagnostics13050912.

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The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we fi

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5

Sekhar, Mr Ch, Ms A. Sharmila, Mr Ch Narayana, et al. "Osteoporosis Diagnosis through Visual Segmentation and Classification: Extensive Review." International Journal of Research Publication and Reviews 5, no. 3 (2024): 3748–53. http://dx.doi.org/10.55248/gengpi.5.0324.0771.

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6

Hyun-Cheol Park, Hyun-Cheol Park, Raman Ghimire Hyun-Cheol Park, Sahadev Poudel Raman Ghimire, and Sang-Woong Lee Sahadev Poudel. "Deep Learning for Joint Classification and Segmentation of Histopathology Image." 網際網路技術學刊 23, no. 4 (2022): 903–10. http://dx.doi.org/10.53106/160792642022072304025.

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<p>Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we

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7

Pandeya, Yagya Raj, Bhuwan Bhattarai, and Joonwhoan Lee. "Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets." Applied Sciences 12, no. 19 (2022): 9571. http://dx.doi.org/10.3390/app12199571.

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This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. We propose two datasets; one is for rhythm classification and one is for segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. A modified HR network and a novel GlocalMuseNet are used for the cl

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8

Vohra, Sumit K., and Dimiter Prodanov. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation." Brain Sciences 11, no. 12 (2021): 1645. http://dx.doi.org/10.3390/brainsci11121645.

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Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform,

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9

Abbas, Khamael, and Mustafa Rydh. "Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm." International Journal of Image, Graphics and Signal Processing 4, no. 10 (2012): 48–53. http://dx.doi.org/10.5815/ijigsp.2012.10.07.

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10

Mittal, Praveen, and Charul Bhatnagar. "Detection of DME by Classification and Segmentation Using OCT Images." Webology 19, no. 1 (2022): 601–12. http://dx.doi.org/10.14704/web/v19i1/web19043.

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Optical Coherence Tomography (OCT) is a developing medical scanning technique proposing non- protruding scanning with high resolution for biological tissues. It is extensively employed in optics to accomplish investigative scanning of the eye, especially the retinal layers. Various medical research works are conducted to evaluate the usage of Optical Coherence Tomography to detect diseases like DME. The current study provides an innovative, completely automated algorithm for disease detection such as DME through OCT scanning. We performed the classification and segmentation for the detection o

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11

Huang, J., L. Xie, W. Wang, X. Li, and R. Guo. "A MULTI-SCALE POINT CLOUDS SEGMENTATION METHOD FOR URBAN SCENE CLASSIFICATION USING REGION GROWING BASED ON MULTI-RESOLUTION SUPERVOXELS WITH ROBUST NEIGHBORHOOD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2022 (June 2, 2022): 79–86. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2022-79-2022.

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Abstract. Point clouds classification is the basis for 3D spatial information extraction and applications. The point-clusters-based methods are proved to be more efficient and accurate than the point-based methods, however, the precision of the classification is significantly affected by the segmentation errors. The traditional single-scale point clouds segmentation methods cannot segment complex objects well in urban scenes which will result in inaccurate classification. In this paper, a new multi-scale point clouds segmentation method for urban scene point clouds classification is proposed.

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12

Hao, Shuang, Yuhuan Cui, and Jie Wang. "Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification." Sensors 21, no. 23 (2021): 7935. http://dx.doi.org/10.3390/s21237935.

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High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an

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13

Hu, Yuan Chun, Jian Sun, and Wei Liu. "Classification-Based Character Segmentation of Image." Applied Mechanics and Materials 519-520 (February 2014): 572–76. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.572.

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In traditional way, the segmentation of image is conducted by simple technology of image processing, which cannot be operated automatically. In this paper, we present a kind of classification method to find the boundary area to segment character image. Referring to sample points and sample areas, the essential segmentation information is extracted. By merging different formats of image transformation, including rotation, erosion and dilation, more features are used to train and test the segmentation model. Parameter tuning is also proposed to optimize the model for promotion. By the means of c

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14

Khan, Khalil, Muhammad Attique, Ikram Syed, and Asma Gul. "Automatic Gender Classification through Face Segmentation." Symmetry 11, no. 6 (2019): 770. http://dx.doi.org/10.3390/sym11060770.

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Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all

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15

Correia, P. L., and F. Pereira. "Classification of Video Segmentation Application Scenarios." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 5 (2004): 735–41. http://dx.doi.org/10.1109/tcsvt.2004.826778.

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16

Cooper, M., T. Liu, and E. Rieffel. "Video Segmentation via Temporal Pattern Classification." IEEE Transactions on Multimedia 9, no. 3 (2007): 610–18. http://dx.doi.org/10.1109/tmm.2006.888015.

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17

Joy, Neenu. "Various OCT Segmentation and Classification Techniques." International Journal of Information Systems and Computer Sciences 9, no. 3 (2020): 31–37. http://dx.doi.org/10.30534/ijiscs/2020/05932020.

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18

Agostini, Valentina, Gabriella Balestra, and Marco Knaflitz. "Segmentation and Classification of Gait Cycles." IEEE Transactions on Neural Systems and Rehabilitation Engineering 22, no. 5 (2014): 946–52. http://dx.doi.org/10.1109/tnsre.2013.2291907.

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19

Hoffman, Richard, and Anil K. Jain. "Segmentation and Classification of Range Images." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 5 (1987): 608–20. http://dx.doi.org/10.1109/tpami.1987.4767955.

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20

Saifullah, Y., and M. T. Manry. "Classification-based segmentation of ZIP codes." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 5 (1993): 1437–43. http://dx.doi.org/10.1109/21.260675.

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21

Borshukov, G. D., G. Bozdagi, Y. Altunbasak, and A. M. Tekalp. "Motion segmentation by multistage affine classification." IEEE Transactions on Image Processing 6, no. 11 (1997): 1591–94. http://dx.doi.org/10.1109/83.641420.

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22

Dikshit, Onkar, and Vinay Behl. "Segmentation-assisted classification for IKONOS imagery." Journal of the Indian Society of Remote Sensing 37, no. 4 (2009): 551–64. http://dx.doi.org/10.1007/s12524-009-0055-1.

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23

Praveena, S., and S. P. Singh. "Segmentation and Classification of Satellite images." World Academics Journal of Engineering Sciences 01, no. 01 (2014): 1006. http://dx.doi.org/10.15449/wjes.2014.1006.

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24

Dam, E. B., and M. Loog. "Efficient Segmentation by Sparse Pixel Classification." IEEE Transactions on Medical Imaging 27, no. 10 (2008): 1525–34. http://dx.doi.org/10.1109/tmi.2008.923961.

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25

Shih, F. Y., and Shy-Shyan Chen. "Adaptive document block segmentation and classification." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 5 (1996): 797–802. http://dx.doi.org/10.1109/3477.537322.

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26

Cesario, Eugenio, Francesco Folino, Antonio Locane, Giuseppe Manco, and Riccardo Ortale. "Boosting text segmentation via progressive classification." Knowledge and Information Systems 15, no. 3 (2007): 285–320. http://dx.doi.org/10.1007/s10115-007-0085-3.

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27

Choi, Hyun-Tae, and Byung-Woo Hong. "Unsupervised Object Segmentation Based on Bi-Partitioning Image Model Integrated with Classification." Electronics 10, no. 18 (2021): 2296. http://dx.doi.org/10.3390/electronics10182296.

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The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this pa

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28

Sun, Jingliang. "Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification." Advances in Mathematical Physics 2021 (October 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/4062200.

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As a means of regulating people’s code of conduct, law has a close relationship with text, and text data has been growing exponentially. Managing and classifying huge text data have become a huge challenge. The PDES image segmentation algorithm is an effective natural language processing method for text classification management. Based on the study of image segmentation algorithm and legal case text classification theory, an image segmentation model based on partial differential equation is proposed, in which diffusion indirectly acts on level set function through auxiliary function. The softw

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29

Jemimma, T., and Y. Jacob Vetharaj. "A Survey on Brain Tumor Segmentation and Classification." International Journal of Software Innovation 10, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijsi.309721.

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Brain tumor segmentation and classification is really a difficult process to identify and detect the tumor region. Magnetic resonance image (MRI) gives valuable information to find the affected area in the brain. The MRI brain image is initially considered, which specifies four various modalities of the brain such as T1, T2, T1C, and the Flair. The preprocessing methodologies and the state-of-the-art MRI-related brain tumor segmentation and classification methods are discussed. This study describes the different types of brain tumor segmentation and classification techniques with its most impo

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30

Zhang, Xiaohua, Hui Wang, Wenxiang Xue, et al. "Research on classification method based on multi-scale segmentation and hierarchical classification." Journal of Physics: Conference Series 2189, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2189/1/012029.

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Abstract This paper transmission line corridors covering area in Hebei north area as the research object to explore multi-scale segmentation threshold suitable for Hebei north image, found applicable to Hebei north region segmentation threshold rules. Main methods are the object-oriented multi-scale segmentation and hierarchical classification, using image segmentation principle, make full use of high resolution image rich features such as shape, texture, object relationships. It is used for the follow-up investigation of hidden danger of external damage of power transmission channel in northe

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31

Hasanpour Zaryabi, E., M. Saadatseresht, and E. Ghanbari Parmehr. "AN OBJECT-BASED CLASSIFICATION FRAMEWORK FOR ALS POINT CLOUD IN URBAN AREAS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 279–86. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-279-2023.

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Abstract. This article presents an automated and effective framework for segmentation and classification of airborne laser scanning (ALS) point clouds obtained from LiDAR-UAV sensors in urban areas. Segmentation and classification are among the main processes of the point cloud. They are used to transform 3D point coordinates into a semantic representation. The proposed framework has three main parts, including the development of a supervoxel data structure, point cloud segmentation based on local graphs, and using three methods for object-based classification. The results of the point cloud s

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32

Salman Al-Shaikhli, Saif Dawood, Michael Ying Yang, and Bodo Rosenhahn. "Brain tumor classification and segmentation using sparse coding and dictionary learning." Biomedical Engineering / Biomedizinische Technik 61, no. 4 (2016): 413–29. http://dx.doi.org/10.1515/bmt-2015-0071.

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AbstractThis paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary cons

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33

Haq, Ejaz Ul, Huang Jianjun, Xu Huarong, Kang Li, and Lifen Weng. "A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI." Computational and Mathematical Methods in Medicine 2022 (August 8, 2022): 1–18. http://dx.doi.org/10.1155/2022/6446680.

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Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate

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34

Sun, Xiaodan, and Xiaofang Sun. "A Pixel Texture Index Algorithm and Its Application." Photogrammetric Engineering & Remote Sensing 90, no. 5 (2024): 277–92. http://dx.doi.org/10.14358/pers.23-00051r2.

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Image segmentation is essential for object-oriented analysis, and classification is a critical parameter influencing analysis accuracy. However, image classification and segmentation based on spectral features are easily perturbed by the high-frequency information of a high spatial resolution remotely sensed (HSRRS) image, degrading its classification and segmentation quality. This article first presents a pixel texture index (PTI) by describing the texture and edge in a local area surrounding a pixel. Indeed.. The experimental results highlight that the HSRRS image classification and segmenta

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35

Pintelas, Emmanuel, and Ioannis E. Livieris. "XSC—An eXplainable Image Segmentation and Classification Framework: A Case Study on Skin Cancer." Electronics 12, no. 17 (2023): 3551. http://dx.doi.org/10.3390/electronics12173551.

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Within the field of computer vision, image segmentation and classification serve as crucial tasks, involving the automatic categorization of images into predefined groups or classes, respectively. In this work, we propose a framework designed for simultaneously addressing segmentation and classification tasks in image-processing contexts. The proposed framework is composed of three main modules and focuses on providing transparency, interpretability, and explainability in its operations. The first two modules are used to partition the input image into regions of interest, allowing the automati

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36

Qiao, Y., T. Chen, J. He, Q. Wen, F. Liu, and Z. Wang. "METHOD OF GRASSLAND INFORMATION EXTRACTION BASED ON MULTI-LEVEL SEGMENTATION AND CART MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1415–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1415-2018.

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It is difficult to extract grassland accurately by traditional classification methods, such as supervised method based on pixels or objects. This paper proposed a new method combing the multi-level segmentation with CART (classification and regression tree) model. The multi-level segmentation which combined the multi-resolution segmentation and the spectral difference segmentation could avoid the over and insufficient segmentation seen in the single segmentation mode. The CART model was established based on the spectral characteristics and texture feature which were excavated from training sam

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37

Kp, Mamatha, and H. N. Suma. "DIAGNOSIS AND CLASSIFICATION OF DEMENTIA USING MRI IMAGES." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (2017): 30–37. http://dx.doi.org/10.29121/granthaalayah.v5.i4racsit.2017.3345.

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The proposed work is to present an effective approach to diagnoseof dementia using MRI images and classify into different stages. There are many manual segmentation algorithms on detection and classification or very simple and specific segmentation algorithms to segment each region of interest exclusively. Thus, the proposed system shall use one of the most effective automatic segmentation techniques on MRI images at once. The regions of interest to segment are CSF (Cerebralspinal fluid), gray matter, and white matter and ventricles using the effective segmentation method called level set segm

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38

Alberto, R. T., S. C. Serrano, G. B. Damian, et al. "OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 45–50. http://dx.doi.org/10.5194/isprsannals-iii-7-45-2016.

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Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal sc

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39

Alberto, R. T., S. C. Serrano, G. B. Damian, et al. "OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 45–50. http://dx.doi.org/10.5194/isprs-annals-iii-7-45-2016.

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Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal sc

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40

Rahman, Fathur, Nuzul Hikmah, and Misdiyanto Misdiyanto. "Analysis Influence Segmentation Image on Classification Image X-raylungs with Method Convolutional Neural." Journal of Informatics Development 2, no. 1 (2023): 23–29. http://dx.doi.org/10.30741/jid.v2i1.1159.

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The impact of image segmentation on the classification of lung X-ray images using Convolutional Neural Networks (CNNs) has been scrutinized in this study. The dataset used in this research comprises 150 lung X-ray images, distributed as 78 for training, 30 for validation, and 42 for testing. Initially, image data undergoes preprocessing to enhance image quality, employing adaptive histogram equalization to augment contrast and enhance image details. The evaluation of segmentation's influence is based on a comparison between image classification with and without the segmentation process. Segmen

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41

A., Afreen Habiba. "Diagnosis of Brain Tumor using Semantic Segmentation and Advance-CNN Classification." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 1204–24. http://dx.doi.org/10.37200/ijpr/v24i5/pr201795.

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42

Dutta, Saibal, Sujoy Bhattacharya, and Kalyan Kumar Guin. "Segmentation and Classification of Indian Domestic Tourists : A Tourism Stakeholder Perspective." Journal of Management and Training for Industries 4, no. 1 (2017): 1–24. http://dx.doi.org/10.12792/jmti.4.1.1.

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43

Lacerda, M. G., E. H. Shiguemori, A. J. Damião, C. S. Anjos, and M. Habermann. "IMPACT OF SEGMENTATION PARAMETERS ON THE CLASSIFICATION OF VHR IMAGES ACQUIRED BY RPAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 43–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-43-2020.

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Abstract. RPAs (Remotely Piloted Aircrafts) have been used in many Remote Sensing applications, featuring high-quality imaging sensors. In some situations, the images are interpreted in an automated fashion using object-oriented classification. In this case, the first step is segmentation. However, the setting of segmentation parameters such as scale, shape, and compactness may yield too many different segmentations, thus it is necessary to understand the influence of those parameters on the final output. This paper compares 24 segmentation parameter sets by taking into account classification

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44

Lee, Jiann-Shu, and Wen-Kai Wu. "Breast Tumor Tissue Image Classification Using DIU-Net." Sensors 22, no. 24 (2022): 9838. http://dx.doi.org/10.3390/s22249838.

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Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network

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45

Khoiro, M., R. A. Firdaus, E. Suaebah, M. Yantidewi, and Dzulkiflih. "Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network." Journal of Physics: Conference Series 2392, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2392/1/012024.

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Abstract The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area

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Alam, Minhaj, Emma J. Zhao, Carson K. Lam, and Daniel L. Rubin. "Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy." Journal of Clinical Medicine 12, no. 1 (2023): 385. http://dx.doi.org/10.3390/jcm12010385.

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With the progression of diabetic retinopathy (DR) from the non-proliferative (NPDR) to proliferative (PDR) stage, the possibility of vision impairment increases significantly. Therefore, it is clinically important to detect the progression to PDR stage for proper intervention. We propose a segmentation-assisted DR classification methodology, that builds on (and improves) current methods by using a fully convolutional network (FCN) to segment retinal neovascularizations (NV) in retinal images prior to image classification. This study utilizes the Kaggle EyePacs dataset, containing retinal photo

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Rathna Priya, T. S., and Annamalai Manickavasagan. "Evaluation of segmentation methods for RGB colour image-based detection of Fusarium infection in corn grains using support vector machine (SVM) and pre-trained convolution neural network (CNN)." Canadian Biosystems Engineering 64, no. 1 (2022): 7.09–7.20. http://dx.doi.org/10.7451/cbe.2022.64.7.9.

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This study evaluated six segmentation methods (clustering, flood-fill, graph-cut, colour-thresholding, watershed, and Otsu’s-thresholding) for segmentation accuracy and classification accuracy in discriminating Fusarium infected corn grains using RGB colour images. The segmentation accuracy was calculated using Jaccard similarity index and Dice coefficient in comparison with the gold standard (manual segmentation method). Flood-fill and graph-cut methods showed the highest segmentation accuracy of 77% and 87% for Jaccard and Dice evaluation metrics, respectively. Pre-trained convolution neural

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Shala, Vegim, and Eliot Bytyçi. "Neural Network Image Segmentation for Sign Language Interpretation." International Journal of Emerging Technology and Advanced Engineering 13, no. 3 (2023): 111–16. http://dx.doi.org/10.46338/ijetae0323_11.

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The use of neural networks to recognize and classify objects in images is a popular field in computer science. It is highly likely that an object in an image chosen for classification will have a representation matrix with significantly less pixels than the background or other elements of the image. As a result, the initial plan would be to divide or segment that object from the other portions of the image that are not essential for categorization. This also serves as the study's objective, for which we employ segmentation to separate the components essential to the classification procedure an

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Latif, Ghazanfar. "DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection." Diagnostics 12, no. 11 (2022): 2888. http://dx.doi.org/10.3390/diagnostics12112888.

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The proper segmentation of the brain tumor from the image is important for both patients and medical personnel due to the sensitivity of the human brain. Operation intervention would require doctors to be extremely cautious and precise to target the brain’s required portion. Furthermore, the segmentation process is also important for multi-class tumor classification. This work primarily concentrated on making a contribution in three main areas of brain MR Image processing for classification and segmentation which are: Brain MR image classification, tumor region segmentation and tumor classific

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Ginley, Brandon, Brendon Lutnick, Kuang-Yu Jen, et al. "Computational Segmentation and Classification of Diabetic Glomerulosclerosis." Journal of the American Society of Nephrology 30, no. 10 (2019): 1953–67. http://dx.doi.org/10.1681/asn.2018121259.

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BackgroundPathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.MethodsWe developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally qu

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