Results 11 to 20 of about 125,098 (262)

Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features [PDF]

Frontiers in Oncology, 2023
AimCancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment.
Aryan Safakish, Aryan Safakish, Aryan Safakish, Lakshmanan Sannachi, Lakshmanan Sannachi, Lakshmanan Sannachi, Daniel DiCenzo, Daniel DiCenzo, Daniel DiCenzo, Christopher Kolios, Christopher Kolios, Christopher Kolios, Christopher Kolios, Ana Pejović-Milić, Gregory J. Czarnota, Gregory J. Czarnota, Gregory J. Czarnota, Gregory J. Czarnota   +17 more
doaj   +2 more sources

Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features [PDF]

Frontiers in Plant Science
BackgroundRapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics.
Yufen Zhang, Yufen Zhang, Feifei Zhu, Kaiming Liang, Zhanhua Lu, Yibo Chen, Xuhua Zhong, Junfeng Pan, Chusheng Lu, Chusheng Lu, Xiangyu Hu, Rui Hu, Meijuan Li, Xinyu Wang, Qunhuan Ye, Yuanhong Yin, Zhaowen Mo, Youqiang Fu   +17 more
doaj   +2 more sources

Carotid Artery Echolucency, Texture Features, and Incident Cardiovascular Disease Events: The MESA Study

Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2019
Background We hypothesized that measures of common carotid artery echolucency and grayscale texture features were associated with cardiovascular disease (CVD) risk factors and could predict CVD events.
Carol C. Mitchell, Claudia E. Korcarz, Adam D. Gepner, Rebecca Nye, Rebekah L. Young, Mika Matsuzaki, Wendy S. Post, Joel D. Kaufman, Robyn L. McClelland, James H. Stein   +9 more
doaj   +3 more sources

Cellular mechanosensing of texture features. [PDF]

Biophys J, 2022
Lin M.
europepmc   +3 more sources

T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods

Sensors, 2021
In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials ...
Claudio Cusano, Paolo Napoletano, Raimondo Schettini   +2 more
doaj   +1 more source

Wood recognition using image texture features. [PDF]

PLoS ONE, 2013
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related ...
Hang-jun Wang, Guang-qun Zhang, Heng-nian Qi   +2 more
doaj   +1 more source

Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma

Scientific Reports, 2021
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis ...
Masaki Ogawa, Satoshi Osaga, Norio Shiraki, Daisuke Kawakita, Nobuhiro Hanai, Tsuneo Tamaki, Satoshi Tsukahara, Takatsune Kawaguchi, Misugi Urano, Yuta Shibamoto   +9 more
doaj   +1 more source

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery. [PDF]

PLoS ONE, 2016
PURPOSE:This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images.
Ahmad Chaddad, Christian Desrosiers, Ahmed Bouridane, Matthew Toews, Lama Hassan, Camel Tanougast   +5 more
doaj   +1 more source

Big Gradient Deformation Monitoring in Open-pit Mine Based on SAR Offsets Tracking Techniques

Taiyuan Ligong Daxue xuebao, 2021
The influence of different parameters on the offset tracking calculation results was analyzed. Then, the offset tracking method based on texture features was added to the offset tracking tool of SNAP8.0.
Jun SUN, Jin ZHANG
doaj   +1 more source

CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma

Cancer Imaging, 2021
Background The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs ...
Menglin Chen, Fu Yin, Yuanmeng Yu, Haijie Zhang, Ge Wen   +4 more
doaj   +1 more source