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Relevant bibliographies by topics / Tissue image segmentation

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Academic literature on the topic 'Tissue image segmentation'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Tissue image segmentation"

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Lee, Seung Hyeun, Sanghyuck Lee, Jaesung Lee, Jeong Kyu Lee, and Nam Ju Moon. "Effective encoder-decoder neural network for segmentation of orbital tissue in computed tomography images of Graves’ orbitopathy patients." PLOS ONE 18, no. 5 (2023): e0285488. http://dx.doi.org/10.1371/journal.pone.0285488.

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Purpose To propose a neural network (NN) that can effectively segment orbital tissue in computed tomography (CT) images of Graves’ orbitopathy (GO) patients. Methods We analyzed orbital CT scans from 701 GO patients diagnosed between 2010 and 2019 and devised an effective NN specializing in semantic orbital tissue segmentation in GO patients’ CT images. After four conventional (Attention U-Net, DeepLab V3+, SegNet, and HarDNet-MSEG) and the proposed NN train the various manual orbital tissue segmentations, we calculated the Dice coefficient and Intersection over Union for comparison. Results C

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Muñoz-Aguirre, Manuel, Vasilis F. Ntasis, Santiago Rojas, and Roderic Guigó. "PyHIST: A Histological Image Segmentation Tool." PLOS Computational Biology 16, no. 10 (2020): e1008349. http://dx.doi.org/10.1371/journal.pcbi.1008349.

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The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool a

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Li, Qun, and Linlin Liu. "Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation." Computational Intelligence and Neuroscience 2022 (June 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/3500592.

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In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificia

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Kong, Zhenglun, Ting Li, Junyi Luo, and Shengpu Xu. "Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning." Journal of Healthcare Engineering 2019 (January 31, 2019): 1–10. http://dx.doi.org/10.1155/2019/2912458.

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Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal flu

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N, Nazeeya Anjum. "A Study on Segmenting Brain Tumor MRI Images." Journal of Computational Science and Intelligent Technologies 2, no. 1 (2021): 1–6. http://dx.doi.org/10.53409/mnaa/jcsit/2101.

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Segmentation of image has traditionally been referred to as the initial stage in image processing. A successful segmentation output will make image processing analysis considerably further easier. There are several image segmentation techniques and methodologies available. Clustering is the most widely used segmentation algorithm for image processing. Segmentation of tumor using magnetic resonance imaging (MRI) data is a critical procedure yet time-consuming process manually carried out by medical specialists. Due to the considerable difference in the tumor tissue appearances across patients,

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Ortiz, Andrés, Antonio A. Palacio, Juan M. Górriz, Javier Ramírez, and Diego Salas-González. "Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors." Computational and Mathematical Methods in Medicine 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/638563.

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Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on

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Liu, Jinhua, Yongsheng Shi, Dongjin Huang, and Jiantao Qu. "Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy." Sensors 25, no. 2 (2025): 565. https://doi.org/10.3390/s25020565.

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The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images. We first construct an EndoTissue dataset of soft tissue

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Hamghalam, Mohammad, Baiying Lei, and Tianfu Wang. "High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4067–74. http://dx.doi.org/10.1609/aaai.v34i04.5825.

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Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block,

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Heidari, Zeinab, Mehrdad Dadgostar, and Zahra Einalou. "AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES." Biomedical Engineering: Applications, Basis and Communications 30, no. 03 (2018): 1850024. http://dx.doi.org/10.4015/s1016237218500242.

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Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to u

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Ramesh, Patil Vinodkumar, Jaware Tushar Hrishikesh, and Manisha S. Patil. "Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1s (2023): 71–79. http://dx.doi.org/10.17762/ijritcc.v11i1s.6002.

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Infant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-med

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Dissertations / Theses on the topic "Tissue image segmentation"

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Lu, Jiang. "Transforms for multivariate classification and application in tissue image segmentation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052195.

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Fakhrzadeh, Azadeh. "Computerized Cell and Tissue Analysis." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-252425.

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The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. Quantitative image analysis is an emerging field and has found its way into analysis of microscopy images for clinical and research purposes. When developing a pipeline, it is important that its components are simple enough to be generalized and have predictive value. This thesis addresses the automation of quantitative analysis of ti

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Purwani, Sri. "Ensemble registration : combining groupwise registration and segmentation." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/ensemble-registration-combining-groupwise-registration-and-segmentation(5f3c06b4-4909-492b-bbc9-a0fecb77d216).html.

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Registration of a group of images generally only gives a pointwise, dense correspondence defined over the whole image plane or volume, without having any specific description of any common structure that exists in every image. Furthermore, identifying tissue classes and structures that are significant across the group is often required for analysis, as well as the correspondence. The overall aim is instead to perform registration, segmentation, and modelling simultaneously, so that the registration can assist the segmentation, and vice versa. However, structural information does play a role in

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Spottiswoode, Bruce Shawn. "Towards automating cine DENSE MRI image analysis : segmentation, tissue tracking and strain computation." Doctoral thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/3204.

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Includes bibliographical references (p. 192-206).<br>Over the past two decades, magnetic resonance imaging (MRI) has developed into a powerful imaging tool for the heart. Imaging cardiac morphology is now commonplace in clinical practice, and a plethora of quantitative techniques have also arisen on the research front. Myocardial tagging is an established quantitative cardiac MRI method that involves magnetically tagging the heart with a set of saturated bands, and monitoring the deformation of these bands as the heart contracts.

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Jeuthe, Julius. "Automatic Tissue Segmentation of Volumetric CT Data of the Pelvic Region." Thesis, Linköpings universitet, Medicinsk informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133153.

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Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationtreatment planning, as it adds prior information about the material composition of imaged tissues. For instance, the separation of tissues into bone, adipose tissue and remaining soft tissues allows to use tabulated material compositions of those tissues. This approximation is not perfect because of variability of tissue composition among patients, but is still better than no approximation at all. Another use for automated tissue segmentationis in model based iterative reconstruction algorithms.

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Nguyễn, Hoài Nam. "Méthodes et algorithmes de segmentation et déconvolution d'images pour l'analyse quantitative de Tissue Microarrays." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S104/document.

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Ce travail de thèse a pour objectif de développer les méthodes originales pour l'analyse quantitative des images de Tissue Microarrays (TMAs) acquises en fluorescence par des scanners dédiés. Nous avons proposé des contributions en traitement d'images portant sur la segmentation des objets d'intérêts (i.e. des échantillons de tissus sur la lame de TMA scannée), la correction des artefacts d'acquisition liés aux scanners en question ainsi que l'amélioration de la résolution spatiale des images acquises en tenant compte des modalités d'acquisition (imagerie en fluorescence) et la conception des

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Nguyen, Lam K. "High-throughput image cytometer for detection of circulating tumor cells and contrast-enhancement filtering for automated 3D image segmentation of cartilage tissue explants." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3259052.

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Thesis (Ph. D.)--University of California, San Diego, 2007.<br>Title from first page of PDF file (viewed June 11, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 223-234).

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Metzger, Andrew. "An automated tissue classification pipeline for magnetic resonance images of infant brains using age-specific atlases and level set segmentation." Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/3143.

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Quantifying tissue volumes in pediatric brains from magnetic resonance (MR) images can provide insight into etiology and onset of neurological disease. Unbiased volumetric analysis can be applied to large population studies when automated image processing is possible. Standard segmentation strategies using adult atlases fail to account for varying tissue contrasts and types associated with the rapid growth and maturational changes seen in early neurodevelopment. The goal of this project was to develop an automated pipeline and two age-specific atlases capable of providing accurate tissue class

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Chaudry, Qaiser Mahmood. "Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47745.

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This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtyp

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Bianculli, Antonella. "Analysis of the scale effect in different computed tomography systems on the evaluation of bone tissue parameters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11207/.

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Tra le patologie ossee attualmente riconosciute, l’osteoporosi ricopre il ruolo di protagonista data le sua diffusione globale e la multifattorialità delle cause che ne provocano la comparsa. Essa è caratterizzata da una diminuzione quantitativa della massa ossea e da alterazioni qualitative della micro-architettura del tessuto osseo con conseguente aumento della fragilità di quest’ultimo e relativo rischio di frattura. In campo medico-scientifico l’imaging con raggi X, in particolare quello tomografico, da decenni offre un ottimo supporto per la caratterizzazione ossea; nello specifico la mic

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Book chapters on the topic "Tissue image segmentation"

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Mosbech, Thomas Hammershaimb, Kasper Pilgaard, Allan Vaag, and Rasmus Larsen. "Automatic Segmentation of Abdominal Adipose Tissue in MRI." In Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_47.

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Harms, Harry, and Hans-Magnus Aus. "Tissue Image Segmentation with Multicolor, Multifocal Algorithms." In Pattern Recognition Theory and Applications. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_42.

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Poot, Dirk H. J., Marleen de Bruijne, Meike W. Vernooij, M. Arfan Ikram, and Wiro J. Niessen. "Improved Tissue Segmentation by Including an MR Acquisition Model." In Multimodal Brain Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24446-9_19.

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Wählby, Carolina, and Ewert Bengtsson. "Segmentation of Cell Nuclei in Tissue by Combining Seeded Watersheds with Gradient Information." In Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_55.

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Vinitski, Simon, Tad Iwanaga, Carlos Gonzalez, David Andrews, Robert Knobler, and John Mack. "Fast tissue segmentation based on a 4D feature map: Preliminary results." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_154.

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Aguirre-Ramos, Hugo, Juan-Gabriel Avina-Cervantes, and Ivan Cruz-Aceves. "Bone Tissue Segmentation Using Spiral Optimization and Gaussian Thresholding." In Bio-Inspired Computing for Image and Video Processing. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315153797-6.

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Kaipala, Jukka, Miguel Bordallo López, Simo Saarakkala, and Jérôme Thevenot. "Automatic Segmentation of Bone Tissue from Computed Tomography Using a Volumetric Local Binary Patterns Based Method." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59129-2_19.

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Wu, Shandong, Susan Weinstein, and Despina Kontos. "Atlas-Based Probabilistic Fibroglandular Tissue Segmentation in Breast MRI." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33418-4_54.

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Yi, Zhao, Antonio Criminisi, Jamie Shotton, and Andrew Blake. "Discriminative, Semantic Segmentation of Brain Tissue in MR Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_68.

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Al-Dmour, Hayat, and Ahmed Al-Ani. "MR Brain Tissue Segmentation Based on Clustering Techniques and Neural Network." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_21.

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Conference papers on the topic "Tissue image segmentation"

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Luan, Xiao, and Jiamin Wang. "Image Semantic Information Fusion for MR Brain Tissue Segmentation." In 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI). IEEE, 2024. https://doi.org/10.1109/medai62885.2024.00081.

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Geißler, Kai, Markus Wenzel, Robert Grimm, et al. "Multi-site segmentation of breast and fibroglandular tissue in MRI with a focus on clinical practicality." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3046890.

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Xie, Zhun, Nan Ji, Lijun Xu, and Jianguo Ma. "Ultrasound Radiofrequency Image Improves the Tissue Segmentation Performance of Deep Learning Models." In 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS). IEEE, 2024. https://doi.org/10.1109/uffc-js60046.2024.10793763.

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Guan, Xi, Junwei Li, and Wei Shao. "Residual Dual Attention Generative Adversarial Networks for Tissue Segmentation on Histopathological Image." In 2024 2nd International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA). IEEE, 2024. http://dx.doi.org/10.1109/prmvia63497.2024.00018.

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Li, Wei-Hua, Yu-Hsing Hsieh, Huei-Fang Yang, and Chu-Song Chen. "PDSeg: Patch-Wise Distillation and Controllable Image Generation for Weakly-Supervised Histopathology Tissue Segmentation." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888097.

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Li, Ning, Dezong Wang, and Paul S. Y. Wu. "Tissue segmentation on CT image." In BiOS Europe '96, edited by Hans-Jochen Foth, Renato Marchesini, and Halina Podbielska. SPIE, 1996. http://dx.doi.org/10.1117/12.260643.

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Singh, Anurag, and Prachi Chauhan. "A REVIEW ON BRAIN TUMOR MRI IMAGE SEGMENTATION." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.53.

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A brain tumor represents one of the most perilous medical conditions. Consequently, swift and precise detection of brain tumors is imperative. Employing automated techniques for identifying brain tumor tissue aids in containing the proliferation of tumor cells within the system. MRI technology is utilized to capture high-resolution images of both the individual's anatomy and cancerous tissues, offering superior image quality compared to other imaging modalities. However, pinpointing brain tumors in MRI images poses challenges due to the intricate nature of the brain. Processing MRI images and

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Yan, H., and J. C. Gore. "Optimized MR image segmentation for tissue characterization." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94547.

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Xu, Zhaoyang, Carlos Fernádez Moro, Danyil Kuznyecov, Béla Bozóky, Le Dong, and Qianni Zhang. "Tissue Region Growing for Hispathology Image Segmentation." In ICBSP 2018: 2018 3rd International Conference on Biomedical Imaging, Signal Processing. ACM, 2018. http://dx.doi.org/10.1145/3288200.3288213.

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Hu, Zepeng, Giulio Anichini, Kevin O'Neill, and Daniel S. Elson. "Multispectral neurosurgery image analysis: preliminary segmentation network evaluated on 47 patient cohort." In Tissue Optics and Photonics III, edited by Zeev Zalevsky, Valery V. Tuchin, and Walter C. Blondel. SPIE, 2024. http://dx.doi.org/10.1117/12.3017483.

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