The analysis of natural textures using run length features

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

“…A total of 22 different textural features were extracted from the GLRLM and GLSZM (Supplementary Table 1). Detailed information on the calculations of each textural feature has been previously reported [16,30,31]. The intensity of FDG uptake in the primary tumour was resampled to 16, 32 and 64 different values to reduce image noise and improve reproducibility [12,32].…”

“…The grey-level run length encoding matrix (GLRLM) [30] and grey-level size zone matrix (GLSZM) [31] were used for assessing the regional textural features. The GLRLM indicates the number of voxel segments with the same intensity in a given direction.…”

Combined Features based Spatial Composite Kernel Formation for Hyperspectral Image Classification

“…The usage of conventional Run Length features such as Short Run Emphasis (SRE), Long Run Emphasis (LRE), Gray Level Non uniformity (GLN), Run Length Non uniformity (RLN) and Run Percentage are explained in [8] and are used for texture analysis. Run Length features are used to analyze the natural textures in [9]. The Dominant Run Length Feature such as Short-run Low Gray Level Emphasis, Short-run High Gray Level Emphasis, Long-run Low Gray Level Emphasis and Long-run High Gray Level Emphasis to extract the discriminant information for successful classification are introduced in [10].…”

Feature space optimization for content-based image retrieval

“…For each data set, the feature vectors were acquired by the feature extractors: Color Moments [19], Co-occurrence [7], Sobel Histogram [2], Histogram [4], Run Length [10] and SIFT [12]. Table 1 shows the number of features extracted and the type of the information captured by each extractor.…”