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Artykuły w czasopismach na temat „Image segmentation”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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Hassan, Raghad Mahdie, and Luma Salal Hassan. "Assessments Image Segmentation Using Genetic Algorithm." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 352–63. http://dx.doi.org/10.46649/fjiece.v3.2.23a.2.6.2024.

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Image segmentation is a crucial technique for processing images. It is a challenging task to process images, and the quality of the segmentation process affects the following assignments, which include classification, object recognition, feature extraction, and object detection.It's a significant phase of a system for computer vision. Image segmentation is the basic problem in many applications for image processing. Over time, image segmentation has gotten more challenging due to its extensive use in numerous applications. It is the procedure of segmenting the image into various areas by using

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Stevens, Michiel, Afroditi Nanou, Leon W. M. M. Terstappen, Christiane Driemel, Nikolas H. Stoecklein, and Frank A. W. Coumans. "StarDist Image Segmentation Improves Circulating Tumor Cell Detection." Cancers 14, no. 12 (2022): 2916. http://dx.doi.org/10.3390/cancers14122916.

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After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood sample

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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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Wang, Guodong, Jie Xu, Qian Dong, and Zhenkuan Pan. "Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/237648.

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Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active co

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Pitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.

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Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MR

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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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Yazdi, Mahsa Badiee, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (2016): 1650025. http://dx.doi.org/10.4015/s1016237216500253.

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Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify

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Yao, Hongtai, Xianpei Wang, Le Zhao, et al. "An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image." Remote Sensing 14, no. 1 (2021): 127. http://dx.doi.org/10.3390/rs14010127.

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The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be re

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Ding, Ruiyao. "Segmentation analysis of UAV images based on Unet deep learning algorithm." Applied and Computational Engineering 54, no. 1 (2024): 248–53. http://dx.doi.org/10.54254/2755-2721/54/20241644.

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The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features,

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Mohd Ghani, Noor Ain Syazwani, and Abdul Kadir Jumaat. "Selective Segmentation Model for Vector-Valued Images." Journal of Information and Communication Technology 21, No.2 (2022): 149–73. http://dx.doi.org/10.32890/jict2022.21.2.1.

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One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevert

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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, et al. "Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/419018.

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This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in

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

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Bharti, Tanwar* Rakesh Kumar Girdhar Gopal. "IMPACT OF IMAGE SEGMENTATION APPROACHES ON NOISY AND BLURRED IMAGES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 354–61. https://doi.org/10.5281/zenodo.54854.

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In today’s world, many applications are used for image processing. Segmentation is one of the main steps used for image processing. Segmentation is used to identify objects in an image. It divides an image into multiple segmentations. There are hundreds of techniques present that are used to segment an image. Clustering is one of the techniques. K nearest neighbor and K-mean techniques are two clustering techniques of segmentation. The main principle of clustering technique is to make cluster of pixels on the basis of distance between pixels and centroids. This paper gives details about

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Jain, Raunak, Faith Lee, Nianhe Luo, Harpreet Hyare, and Anand S. Pandit. "A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation." NeuroSci 5, no. 3 (2024): 265–75. http://dx.doi.org/10.3390/neurosci5030021.

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The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-process

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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, et al. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variabili

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Li, Yuan, Fu Cang Jia, Xiao Dong Zhang, Cheng Huang, and Huo Ling Luo. "Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation." Applied Mechanics and Materials 333-335 (July 2013): 1065–70. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1065.

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The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Est

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Cardone, Barbara, Ferdinando Di Martino, and Vittorio Miraglia. "A Novel Fuzzy-Based Remote Sensing Image Segmentation Method." Sensors 23, no. 24 (2023): 9641. http://dx.doi.org/10.3390/s23249641.

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Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-base

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Bai, Shurui, Zhuo Deng, Jingyan Yang, et al. "FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts." Bioengineering 11, no. 9 (2024): 950. http://dx.doi.org/10.3390/bioengineering11090950.

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The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency

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Xiang, Ming, Zhen Dong Cui, and Yuan Hong Wu. "A Fingerprint Image Segmentation Method Based on Fractal Dimension." Advanced Materials Research 461 (February 2012): 299–301. http://dx.doi.org/10.4028/www.scientific.net/amr.461.299.

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Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations

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Gaikwad, Akshay V., and Suyash Awate. "Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations." Machine Learning for Biomedical Imaging 2, June 2024 (2024): 717–60. http://dx.doi.org/10.59275/j.melba.2024-2fgd.

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Typical methods for semantic image segmentation rely on large training sets comprising per-pixel semantic segmentations. In medical-imaging applications, obtaining a large number of expert segmentations can be difficult because of the underlying demands on the experts’ time and the budget. However, in many such applications, it is much easier to obtain image-level information indicating the class labels of the objects of interest present in the image. We propose a novel deep-neural-network (DNN) framework for the semantic segmentation of images relying on weakly-and-semi-supervised learning fr

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Beasley, Ryan A. "Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector." ISRN Signal Processing 2012 (May 17, 2012): 1–9. http://dx.doi.org/10.5402/2012/914232.

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Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient-specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation algorithm combining the region-growing Seeded Cellular Automata with a boundary term based on an edge-detected image. Both single processor and parallel processor implementations are developed and the algorithm is shown to be suitable for quick segmentations (2.2 s for voxel brain MRI) and interactive supervision (2–220 Hz). Furtherm

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Ikokou, Guy Blanchard, and Kate Miranda Malale. "Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications." Geomatics 4, no. 2 (2024): 149–72. http://dx.doi.org/10.3390/geomatics4020009.

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Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from

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Arora, Jyoti, and Meena Tushir. "Intuitionistic Level Set Segmentation for Medical Image Segmentation." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 1039–46. http://dx.doi.org/10.2174/2213275912666190218150045.

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Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by

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Xu, Yuhang. "Application of Image Segmentation Algorithms in Computer Vision." Frontiers in Computing and Intelligent Systems 7, no. 3 (2024): 17–20. http://dx.doi.org/10.54097/gq1s6737.

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In the field of computer vision (CV), image segmentation technology, as a fundamental part, has a crucial impact on the accuracy of subsequent image processing tasks. Image segmentation is not only a crucial transitional step from image processing to image analysis, but also a hot and difficult research topic in the field of CV. Although significant progress has been made in the research of image segmentation algorithms, existing segmentation algorithms may still face challenges in certain specific scenarios due to the complexity and diversity of images, making it difficult to achieve ideal se

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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main s

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Zhou, Zhi Heng, and Hui Qiang Zhong. "Image Segmentation Based on Poisson Equation." Applied Mechanics and Materials 284-287 (January 2013): 3131–34. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3131.

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Image segmentation is an important part of the image processing. Currently, image segmentation methods are mainly the threshold-based segmentation method, the region-based segmentation method, the edge-based segmentation method and the Snake model based on energy function etc. This paper presents a novel image segmentation method based on the Poisson equation. The goal of the segmentation method is to divide the image into two homogeneous parts, the boundary portion and the non-boundary portion, which have similar gray values in homogeneous part. The key of the method is to build a Poisson equ

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Channappa A. "Comparative analysis of image segmentation algorithms for pattern recognition." World Journal of Advanced Research and Reviews 5, no. 3 (2020): 193–99. https://doi.org/10.30574/wjarr.2020.5.3.0048.

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Image Segmentation is one of the hopeful and emerging fields in image processing. The examined algorithms are compared according to the quality of its operation at distorted images with respect to ground-truth images. It has applications in various fields like medical applications, astronomical, traffic controlling, Fingerprint recognition, digital forensics, self-driving motor cars, locating objects in satellite images etc. It is the process of splitting an image into sub regions with respect to one or more characteristics. Image segmentation is the basic step to analyse images and extract da

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Shah, Nilima, Dhanesh Patel, and Pasi Fränti. "Fast Mumford-Shah Two-Phase Image Segmentation Using Proximal Splitting Scheme." Journal of Applied Mathematics 2021 (April 13, 2021): 1–13. http://dx.doi.org/10.1155/2021/6618505.

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The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metric

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Orlando, Nathan, Igor Gyacskov, Derek J. Gillies, et al. "Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound." Physics in Medicine & Biology 67, no. 7 (2022): 074002. http://dx.doi.org/10.1088/1361-6560/ac5a93.

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Abstract Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image qua

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Priego, Torres Blanca María, and Richard J. Duro. "An approach for the customized high-dimensional segmentation of remote sensing hyperspectral images." Sensors 19, no. 13 (2019): 2887. https://doi.org/10.3390/s19132887.

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The work presents a methodology for the customized segmentation of remote sensing hyperspectral images using a multigradient cellular automaton (MGCA) approach coupled with an evolutionary algorithm (ECAS-II). The study addresses three main challenges in hyperspectral image segmentation: the need for segmentations tailored to user requirements, the scarcity of adequately labeled reference images, and the loss of information that occurs when high-dimensional images are projected into lower-dimensional spaces before segmentation. The proposed methodology allows for the segmentation of multidimen

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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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Cahuina, Edward Cayllahua, Jean Cousty, Yukiko Kenmochi, Arnaldo de Albuquerque Araújo, Guillermo Cámara-Chávez, and Silvio Jamil F. Guimarães. "Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (2019): 1940008. http://dx.doi.org/10.1142/s0218001419400081.

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Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenloch

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MEZARIS, VASILEIOS, IOANNIS KOMPATSIARIS, and MICHAEL G. STRINTZIS. "STILL IMAGE SEGMENTATION TOOLS FOR OBJECT-BASED MULTIMEDIA APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 04 (2004): 701–25. http://dx.doi.org/10.1142/s0218001404003393.

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In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow

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Yahya, Rafaa I., Siti Mariyam Shamsuddin, Salah I. Yahya, Bisan Alsalibi, and Ghada K. Al-Khafaji. "Membrane Computing for Real Medical Image Segmentation." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 6, no. 2 (2018): 27. http://dx.doi.org/10.14500/aro.10442.

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In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in

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Anbarasan, Kalaivani, and S. Chitrakala. "Clustering-Based Color Image Segmentation Using Local Maxima." International Journal of Intelligent Information Technologies 14, no. 1 (2018): 28–47. http://dx.doi.org/10.4018/ijiit.2018010103.

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Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, wit

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Muniappan, Ramaraj, Thiruvenkadam Thangavel, Govindaraj Manivasagam, et al. "Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2803. http://dx.doi.org/10.11591/ijece.v14i3.pp2803-2813.

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In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework f

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Gao, Song, Chengcui Zhang, and Wei-Bang Chen. "Color Image Segmentation." International Journal of Multimedia Data Engineering and Management 3, no. 3 (2012): 66–82. http://dx.doi.org/10.4018/jmdem.2012070104.

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An intuitive way of color image segmentation is through clustering in which each pixel in an image is treated as a data point in the feature space. A feature space is effective if it can provide high distinguishability among objects in images. Typically, in the preprocessing phase, various modalities or feature spaces are considered, such as color, texture, intensity, and spatial information. Feature selection or reduction can also be understood as transforming the original feature space into a more distinguishable space (or subspaces) for distinguishing different content in an image. Most clu

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Li, Jianzhang, Sven Nebelung, Björn Rath, Markus Tingart, and Jörg Eschweiler. "A novel combined level set model for automatic MR image segmentation." Current Directions in Biomedical Engineering 6, no. 3 (2020): 20–23. http://dx.doi.org/10.1515/cdbme-2020-3006.

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AbstractMedical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities ar

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Li, Bing, Shaoyong Wu, Siqin Zhang, Xia Liu, and Guangqing Li. "Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm." Tomography 8, no. 1 (2022): 59–76. http://dx.doi.org/10.3390/tomography8010006.

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Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscali

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R, Asharani, and Naveen Kumar R. "Review on Brain Tumor Image Segmentation in Time-Frequency Domain." Journal of Image Processing and Artificial Intelligence 8, no. 3 (2022): 1–6. http://dx.doi.org/10.46610/joipai.2022.v08i03.001.

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The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medica

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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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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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Zhou, Yang, Lijuan Zhu, and Dong Ma. "Research on Traditional Image Segmentation Method Based on Oil Drilling Pipe Defects." Journal of Physics: Conference Series 2639, no. 1 (2023): 012057. http://dx.doi.org/10.1088/1742-6596/2639/1/012057.

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Abstract This study explores the feasibility and efficacy of conventional image seg-mentation technology in diagnosing failures in oil drilling pipe images. Simultaneously, it envisions an intelligent approach to diagnose defects in oil drilling pipes. The present paper examines and scrutinizes traditional image segmentation methods in light of the characteristics of oil drilling pipe defect images. It devises experiments tailored for these defect images and employs various traditional image segmentation methods to facilitate comparison and evaluation. The experimental findings illustrate that

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Khudov, Hennadii, Oleksandr Makoveichuk, Irina Khizhnyak, et al. "The Choice of Quality Indicator for the Image Segmentation Evaluation." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (2022): 95–103. http://dx.doi.org/10.46338/ijetae1022_11.

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In this paper, the main attention is focused on the stage of image segmentation and on the choice of a quality indicator for the evaluation of image segmentation. In general form the segmentation problem of color and tone images is formulated in. The main approaches that are used in methods and techniques of image segmentation are highlighted. The need to evaluate the results of the work of methods and techniques of image segmentation to assess their performance has been established. The options for the image segmentation quality assessing of both at the objective (quantitative) level and at t

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Tan, Jianquan, Wenrui Zhou, Ling Lin, and Huxidan Jumahong. "A Review of Semantic Medical Image Segmentation Based on Different Paradigms." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–25. http://dx.doi.org/10.4018/ijswis.345246.

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In recent years, with the widespread application of medical images, the rapid and accurate identification of these regions of interest in a large number of medical images has received widespread attention. This article provides a review of medical image segmentation methods based on deep learning. Firstly, an overview of medical image segmentation methods was provided in the relevant knowledge, segmentation types, segmentation processes, and image processing applications. Secondly, the applications of supervised, semi supervised, and unsupervised methods in medical image segmentation were disc

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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.

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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thres

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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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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 (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 pix

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Tian, Yan, Chong Wu Ruan, and Chen Hong Sui. "Study on the Optimal Image Resolution for Image Segmentation." Applied Mechanics and Materials 665 (October 2014): 724–32. http://dx.doi.org/10.4028/www.scientific.net/amm.665.724.

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Resolution is one of the basic and key indexes on assessing the quality of remote sensing image. However, it can not be concluded that the higher the image resolution, the better the segmentation result, since high resolution image contains not only more details of interested object, but also more redundant information of background which causes much difficulty on image segmentation and target recognition. To determine an optimal image resolution for image segmentation, an image pyramid with resolution continuously changing is built by down sampling and super-resolution techniques at first, an

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (2021): 1–5. http://dx.doi.org/10.35940/ijipr.b1002.061221.

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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique

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