Results 1 to 10 of about 15,845 (235)

Computed Tomography Radiomic Signatures Associated With Neutrophil Extracellular Trap Enrichment and First-Pass Outcome in Ischemic Stroke Thrombi. [PDF]

J Am Heart Assoc
Santo BA, Patel TR, Mousavi Janbeh Sarayi SM, Poppenberg KE, Balghonaim S, Scotti A, Jenkins TD, Jaikumar V, Levy EI, Siddiqui AH, Kolega J, Tutino VM.   +11 more
europepmc   +1 more source

Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach. [PDF]

BMC Oral Health
Bahaa S, Ramadan O, Darwish ZE, El-Fiky E, Saleh MM.   +4 more
europepmc   +1 more source

Adaptive multi-feature fusion architecture with optimized learning for high-fidelity brain tumor classification in MRI. [PDF]

Sci Rep
Safy M, Abd-Ellah MK, Bayoumi ES, Salama GM.   +3 more
europepmc   +1 more source

Texture Analysis of Histology Images for Characterizing Ultrasound-Stimulated Microbubble Radiation Enhancement Treatment Response. [PDF]

Cells
Sannachi L, Mohabir S, McNabb E, Sharma D, Giles A, Yang W, Leong KX, Stanisz M, Czarnota GJ.   +8 more
europepmc   +1 more source

Pattern-based image retrieval using GLCM

Neural Computing and Applications, 2018
Gray-level co-occurrence matrix (GLCM) is one of the oldest techniques used for texture analysis. It has two important parameters, i.e., distance and direction. In this paper, various combinations of distance and directional angles used for GLCM calculation are analyzed in order to recognize certain patterned images based on their textural features. In
Divya Srivastava, B Rajitha, Suneeta Agarwal   +2 more
exaly   +2 more sources

MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification

Computerized Medical Imaging and Graphics, 2023
Distinguishing malignant from benign lesions has significant clinical impacts on both early detection and optimal management of those early detections. Convolutional neural network (CNN) has shown great potential in medical imaging applications due to its powerful feature learning capability.
Shu Zhang 0006, Jinru Wu, Enze Shi, Sigang Yu, Yongfeng Gao, Lihong Li 0002, Licheng Ryan Kuo, Marc Jason Pomeroy, Jerome Zhengrong Liang   +8 more
openaire   +2 more sources

GLCM and its application in pattern recognition

2017 5th International Symposium on Computational and Business Intelligence (ISCBI), 2017
Grey Level Co-Occurrence matrix is one of the oldest techniques used for texture analysis. The Grey Level Co-Occurrence matrix has two important parameters i.e. distance and direction. In this paper various combinations of distance and directional angles used for GLCM calculation are analyzed in order to recognize certain patterned images based on ...
Shruti Singh, Divya Srivastava, Suneeta Agarwal   +2 more
openaire   +1 more source

GLCM and Fuzzy Clustering for Ocean Features Classification

2010 International Conference on Machine Vision and Human-machine Interface, 2010
since Seasat lunched in 1978, much understanding has been gained on the potential of synthetic aperture radar (SAR) technology in oceanography. In this paper, the ocean features, i.e., internal waves, ocean fronts, present in SAR images are discussed.
Ronghua Tao, Jie Chen, Biao Chen, Cuihua Liu   +3 more
openaire   +1 more source

Fast GLCM-based Intra Block Partition for VVC

2021 Data Compression Conference (DCC), 2021
In the latest video coding standard, Versatile Video Coding (H.266/VVC), a new quadtree with nested multi-type tree (QTMTT) coding block structure is proposed. QTMTT significantly improves coding performance, but more complex block partitioning structure brings greater computational burden.
Huanchen Zhang, Li Yu 0003, Tiansong Li, Hongkui Wang   +3 more
openaire   +1 more source