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Journal articles on the topic 'Imagem segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Conti, Luis Américo, and Murilo Baptista. "SYNTHETIC APERTURE SONAR IMAGES SEGMENTATION USING DYNAMICAL MODELING ANALYSIS." Revista Brasileira de Geofísica 31, no. 3 (2013): 455. http://dx.doi.org/10.22564/rbgf.v31i3.315.

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ABSTRACT. Symbolic Models applied to Synthetic Aperture Sonar images are proposed in order to assess the validity and reliability of use of such models and evaluate how effective they can be in terms of image classiﬁcation and segmentation. We developed an approach for the description of sonar images where the pixels distribution can be transformed into points in the symbolic space in a similar way as symbolic space can encode a trajectory of a dynamical system. One of the main characteristic of approach is that points in the symbolic space are mapped respecting dynamical rules and, as a conse

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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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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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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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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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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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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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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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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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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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Yang, Yong, Shuying Huang, and Nini Rao. "An Automatic Hybrid Method for Retinal Blood Vessel Extraction." International Journal of Applied Mathematics and Computer Science 18, no. 3 (2008): 399–407. http://dx.doi.org/10.2478/v10006-008-0036-5.

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An Automatic Hybrid Method for Retinal Blood Vessel ExtractionThe extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clusteri

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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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Wan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, and Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net." Sensors 20, no. 16 (2020): 4473. http://dx.doi.org/10.3390/s20164473.

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Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which

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Warfield, Simon K., Kelly H. Zou, and William M. Wells. "Validation of image segmentation by estimating rater bias and variance." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1874 (2008): 2361–75. http://dx.doi.org/10.1098/rsta.2008.0040.

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The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a ‘ground truth’ or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically

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Lee, Aaron Y., Cecilia S. Lee, Pearse A. Keane, and Adnan Tufail. "Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation." Journal of Ophthalmology 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6571547.

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Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework.Methods.A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to$0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface

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Liu, Hongbing, Xiaoyu Diao, and Huaping Guo. "Quantitative analysis for image segmentation by granular computing clustering from the view of set." Journal of Algorithms & Computational Technology 13 (January 2019): 174830181983305. http://dx.doi.org/10.1177/1748301819833050.

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As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular computing clustering algorithms are induced by the distance formulas, the granules with different shapes are defined as the forms of vectors by different distance norms, especially, the atomic granule is induced by a point of space, the union operator realizes the transformation between two granule spaces

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Kollem, Sreedhar, Katta Rama Linga Reddy, and Duggirala Srinivasa Rao. "A Review of Image Denoising and Segmentation Methods Based on Medical Images." International Journal of Machine Learning and Computing 9, no. 3 (2019): 288–95. http://dx.doi.org/10.18178/ijmlc.2019.9.3.800.

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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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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, et al. "Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation." Computational and Mathematical Methods in Medicine 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/190304.

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This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the hu

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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, and M. A. Ibarra-Manzano. "Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/909625.

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This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the ini

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Ma, Yushu, Yingzhe Gao, Zhaolin Li, et al. "Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering." Journal of Innovative Optical Health Sciences 14, no. 01 (2021): 2140011. http://dx.doi.org/10.1142/s1793545821400113.

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Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolati

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Akcay, Ozgun, Emin Avsar, Melis Inalpulat, Levent Genc, and Ahmet Cam. "Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery." ISPRS International Journal of Geo-Information 7, no. 11 (2018): 424. http://dx.doi.org/10.3390/ijgi7110424.

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Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However

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Campbell, N. W., B. T. Thomas, and T. Troscianko. "Automatic Segmentation and Classification of Outdoor Images Using Neural Networks." International Journal of Neural Systems 08, no. 01 (1997): 137–44. http://dx.doi.org/10.1142/s0129065797000161.

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The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

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Yazdani, S., R. Yusof, A. Karimian, A. H. Riazi, and M. Bennamoun. "A Unified Framework for Brain Segmentation in MR Images." Computational and Mathematical Methods in Medicine 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/829893.

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Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector ma

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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, et al. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (July 17, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.1.

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Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three

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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, et al. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (October 9, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.2.

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Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three

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Liu, Ming, Shichao Chen, Fugang Lu, Mengdao Xing, and Jingbiao Wei. "Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion." Sensors 21, no. 5 (2021): 1643. http://dx.doi.org/10.3390/s21051643.

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For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the

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Wei, Yun Tao, and Yi Bing Zhou. "Segmentations of Liver and Hepatic Tumors from 3D Computed Tomography Abdominal Images." Advanced Materials Research 898 (February 2014): 684–87. http://dx.doi.org/10.4028/www.scientific.net/amr.898.684.

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The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from seve

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Wählby, Carolina, Joakim Lindblad, Mikael Vondrus, Ewert Bengtsson, and Lennart Björkesten. "Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells." Analytical Cellular Pathology 24, no. 2-3 (2002): 101–11. http://dx.doi.org/10.1155/2002/821782.

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Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of corr

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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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Tetard, Martin, Ross Marchant, Giuseppe Cortese, Yves Gally, Thibault de Garidel-Thoron, and Luc Beaufort. "Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow." Climate of the Past 16, no. 6 (2020): 2415–29. http://dx.doi.org/10.5194/cp-16-2415-2020.

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Abstract. Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new wo

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Guo, Yu, Yuanming Feng, Jian Sun, et al. "Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model." Computational and Mathematical Methods in Medicine 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/401201.

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The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint d

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Schmidt-Richberg, A., J. Fiehler, T. Illies, et al. "Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation." Methods of Information in Medicine 50, no. 01 (2011): 74–83. http://dx.doi.org/10.3414/me10-02-0003.

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Summary Objectives: Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been prop

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Li, Qingyun, Zhibin Yu, Yubo Wang, and Haiyong Zheng. "TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation." Sensors 20, no. 15 (2020): 4203. http://dx.doi.org/10.3390/s20154203.

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The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we i

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Jo, Hang-Chan, Hyeonwoo Jeong, Junhyuk Lee, Kyung-Sun Na, and Dae-Yu Kim. "Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm." Sensors 21, no. 9 (2021): 3224. http://dx.doi.org/10.3390/s21093224.

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The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve hi

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Cruz-Aceves, I., J. G. Aviña-Cervantes, J. M. López-Hernández, and S. E. González-Reyna. "Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/132953.

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This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of seque

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Hage, Ilige S., and Ramsey F. Hamade. "Statistical and Physical Micro-Feature-Based Segmentation of Cortical Bone Images Using Artificial Intelligence." Materials Science Forum 783-786 (May 2014): 222–27. http://dx.doi.org/10.4028/www.scientific.net/msf.783-786.222.

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At the micro scale, dense cortical bone is structurally comprised mainly of Osteon units that contain Haversian canals, lacunae, and concentric lamellae solid matrix. Osteons are separated from each other by cement lines. These microfeatures of cortical bone are typically captured in digital histological images. In this work, we aim to automatically segment these features utilizing optimized pulse coupled neural networks (PCNN). These networks are artificially intelligent (AI) tools that can model neural activity and produce a series of binary pulses (images) representing the segmentations of

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

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

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Kerz, Maximilian, Amos Folarin, Ruta Meleckyte, Fiona M. Watt, Richard J. Dobson, and Davide Danovi. "A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data." Journal of Biomolecular Screening 21, no. 9 (2016): 887–96. http://dx.doi.org/10.1177/1087057116652064.

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Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmenta

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

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

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Khan, Muhammad Burhan, Humaira Nisar, Choon Aun Ng, Kim Ho Yeap, and Koon Chun Lai. "Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment." Microscopy and Microanalysis 23, no. 6 (2017): 1130–42. http://dx.doi.org/10.1017/s1431927617012673.

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AbstractImage processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of i

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Vania, Malinda, Dawit Mureja, and Deukhee Lee. "Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels." Journal of Computational Design and Engineering 6, no. 2 (2019): 224–32. http://dx.doi.org/10.1016/j.jcde.2018.05.002.

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Abstract There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study,

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Handels, H., J. Ehrhardt, and A. Schmidt-Richberg. "Integrated Segmentation and Non-linear Registration for Organ Segmentation and Motion Field Estimation in 4D CT Data." Methods of Information in Medicine 48, no. 04 (2009): 344–49. http://dx.doi.org/10.3414/me9234.

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Summary Objectives: The development of spatiotemporal tomographic imaging techniques allows the application of novel techniques for diagnosis and therapy in the medical routine. However, in consequence to the increasing amount of image data automatic methods for segmentation and motion estimation are required. In adaptive radiation therapy, registration techniques are used for the estimation of respiration-induced motion of pre-segmented organs. In this paper, a variational approach for the simultaneous computation of segmentations and a dense non-linear registration of the 3D images of the se

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Lormand, Charline, Georg F. Zellmer, Károly Németh, et al. "Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks." Microscopy and Microanalysis 24, no. 6 (2018): 667–75. http://dx.doi.org/10.1017/s1431927618015428.

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AbstractCrystals within volcanic rocks record geochemical and textural signatures during magmatic evolution before eruption. Clues to this magmatic history can be examined using crystal size distribution (CSD) studies. The analysis of CSDs is a standard petrological tool, but laborious due to manual hand-drawing of crystal margins. The trainable Weka segmentation (TWS) plugin in ImageJ is a promising alternative. It uses machine learning and image segmentation to classify an image. We recorded back-scattered electron (BSE) images of three volcanic samples with different crystallinity (35, 50 a

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Wu, Chia Hsiang, and Mei Yun Su. "Specular Highlight Detection from Endoscopic Images for Shape Reconstruction." Applied Mechanics and Materials 870 (September 2017): 357–62. http://dx.doi.org/10.4028/www.scientific.net/amm.870.357.

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Endoscopy provides a convenient way to access the inner structures of various organs. The endoscopic images provide an immediate observation and help diagnosis and therapy. Shape reconstruction from endoscopic images further provides real scale factor for image-guided navigation. However, specular highlights, bright patches of light appearing on the imaged surface, mask the real image texture and result in erroneous reconstruction. Therefore, the detection of specular highlights is essential for accurate reconstruction. In this study, we divide the images into homogeneous regions by color quan

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G, Mohandass, Hari Krishnan G, and Hemalatha R J. "An approach to automated retinal layer segmentation in SDOCT images." International Journal of Engineering & Technology 7, no. 2.25 (2018): 56. http://dx.doi.org/10.14419/ijet.v7i2.25.12371.

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The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise de

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