Textural features corresponding to textural properties

By M. Amadasun, R. King. 1989

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Describing Textures in the Wild (2013, background): “The study of perceptual attributes of textures has a long history starting from pre-attentive aspects and grouping [16], to coarse high-level attributes [1, 2, 33], to some recent work aimed at discovering such attributes by automatically mining descriptions of images from the Internet [3, 12].”
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016, methodology): “stance entropy Let p ij= d ij=N s. Zone distance entropy is then deﬁned as: F dzm:zd:entr = XN g i=1 XN d j=1 p ijlog 2 p ij 4.10 Textural features - Neighbourhood grey tone difference based features Amadasun and King (1989) introduced an alternative grey level matrix. The neighbourhood grey tone di”
Content-Based Image Retrieval Systems (2001, background): “According to Amadasun and King [2]: “Literally, texture refers to the arrangement of the basic constituents of a material.”
The role of quantitative PET in predicting cancer treatment outcomes (2014, background): “The NGTDM is thought to provide more humanlike perception of texture such as (Table 3): coarseness, contrast, busyness, and complexity [64].”
Deep convolutional filter banks for texture recognition and segmentation (2014, background): “Hence, a signiﬁcant amount of effort in the computer vision community has gone into recognizing texture via tasks such as texture perception [1, 2, 12, 13] and description [7, 11], material recognition [25, 34, 35], segmentation [20, 28], and even synthesis [9, 40].”
Introduction to Radiomics (2020, background): “Proposed by Amadasun and King (19), the NGTDM quantifies the sum of differences between the gray level of a pixel or voxel and the mean gray level of its neighboring pixels or voxels within a predefined distance.”
Texture-based classification of atherosclerotic carotid plaques (2003, background): “Amadasun and King [9] proposed the NGTDM in order to”
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer (2011, unknown): “Sex Male 35 (85) Female 6 (15) Primary site Upper esophagus 10 (24) Middle esophagus 15 (37) Lower esophagus 16 (39) Tumor cell type Squamous cell carcinoma 31 (76) Adenocarcinoma 10 (24) Histologic grade Well differentiated 12 (29) Moderately differentiated 11 (27) Poorly differentiated 3 (7) Unkno”
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope (2001, background): “Several studies have proposed perceptual properties for representing texture images (Amadasun and King, 1989; Rao and Lohse, 1993; Tamura et al., 1978; Heaps and Handel, 1999).”
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities (2015, methodology): “This uncertainty on g(xi) = 0 incorporates 19 patients of our cohort (9 from the LungMets group, 10 from the NoLungMets group), and no definitive conclusion could be drawn for these patients in a clinical decision-support system.”
Deep filter banks for texture recognition and segmentation (2015, background): “Hence, a significant amount of effort in the computer vision community has gone into recognizing texture via tasks such as texture perception [1, 2, 12, 13] and description [7, 11], material recognition [26, 36, 37], segmentation [20, 29], and even synthesis [9, 43].”