Results 11 to 20 of about 276,921 (315)

Color Texture Analysis: A Survey

IEEE Access, 2022
In the field of image processing, texture features and color are fundamental visual cue with complementary roles. They are used in many applications and in a large variety of areas such as quality control, content-based image retrieval, remote sensing ...
Anne Humeau-Heurtier
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Multiscale Analysis for Improving Texture Classification

Applied Sciences, 2023
Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.
Steve Tsham Mpinda Ataky, Diego Saqui, Jonathan de Matos, Alceu de Souza Britto Junior, Alessandro Lameiras Koerich   +4 more
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Texture Analysis using wavelet Transform

Advances in Distributed Computing and Artificial Intelligence Journal, 2020
In this research application of wavelet based multiscale image analysis methods for texture analysis has been highlighted. These methods are based on multiresolution properties of the two-dimensional wavelet transform, which is used to extract the ...
Vinay priy Mishra
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Magnetic resonance imaging in cervical cancer: current opportunities of radiomics analysis and prospects for its further developmen

Акушерство, гинекология и репродукция, 2023
Introduction. Due to the dynamic development of modern imaging technologies in recent years, much attention has been paid to radiomics particularly texture analysis. The complexity of clinically evaluated tumor procession in cervical cancer (CC) accounts
A. E. Solopova, J. V. Nosova, B. B. Bendzhenova   +2 more
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Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma [PDF]

, 2021
Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating ...
Yamashiro, Tsuneo, Tsubakimoto, Maho, Mimura, Hidefumi, Iida, Gyo, Tomita, Hayato, Murayama, Sadayuki   +5 more
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Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study

Applied Sciences, 2022
Radiomics is emerging as a promising tool to extract quantitative biomarkers—called radiomic features—from medical images, potentially contributing to the improvement in diagnosis and treatment of oncological patients.
João Santinha, Linda Bianchini, Mário Figueiredo, Celso Matos, Alessandro Lascialfari, Nikolaos Papanikolaou, Marta Cremonesi, Barbara A. Jereczek-Fossa, Francesca Botta, Daniela Origgi   +9 more
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Colour Texture Analysis [PDF]

, 2008
This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour ...
Whelan, Paul F., Ghita, Ovidiu
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Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis

Journal of Imaging, 2022
(1) Background: Segmentation of the bladder inner’s wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function.
Rania Trigui, Mouloud Adel, Mathieu Di Bisceglie, Julien Wojak, Jessica Pinol, Alice Faure, Kathia Chaumoitre   +6 more
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Enriching texture analysis with semantic data [PDF]

, 2013
We argue for the importance of explicit semantic modelling in human-centred texture analysis tasks such as retrieval, annotation, synthesis, and zero-shot learning.To this end, low-level attributes are selected and used to define a semantic space for ...
Tim Matthews, Mark S. Nixon, Matthews, Tim, Niranjan, Mahesan, Mahesan Niranjan, Nixon, Mark S., Mark S Nixon   +6 more
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Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor

Diagnostics, 2023
Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis.
Hyeongyu Yoo, Kyu Ran Cho, Sung Eun Song, Yongwon Cho, Seung Pil Jung, Kihoon Sung   +5 more
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