Skip to main content

Table 3 The summary of the statistical model used in texture quantification

From: The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

 

Statistical Model

First-order

Second-order

Higher-order

Meaning

Frequency distribution of pixel/voxel gray-values without considering their spatial orientation [45].

Spatial distribution of pixel/voxel gray-levels in relation to their relative positions [46]

Characterizing images based on a unique interaction between the pixels/voxels that constitute the image [25].

Computation method

Histogram from which several texture features can be derived

Texture features obtained from the joint probability distribution of neighboring pixels

Mathematical algorithms that evaluate pixel intensities in relation to their neighboring pixels

Examples

mean gray-level intensity, uniformity, entropy, standard deviation, skewness, kurtosis

GLCM, GLRLM

NGTDM, NSZM, wavelet, and Gabor transform

  1. GLCM gray-level co-occurrence matrix, GLRLM gray-level run-length matrix, NGTDM neighborhood gray-tone difference matrix, NSZM neighborhood size zone matrix
Back to article page

Cancer Imaging

ISSN: 1470-7330

Contact us