Application of texture analysis to ventilation SPECT/CT data

Abstract

It is demonstrated that textural parameters calculated from functional pulmonary CT data have the potential to provide a robust and objective quantitative characterisation of inhomogeneity in lung function and classification of lung diseases in routine clinical applications. Clear recommendations are made for optimum data preparation and textural parameter selection.A new set of platform-independent software tools are presented that are implemented as plug-ins 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 "Application of texture analysis to functional pulmonary CT data", Meier et al. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 The potential for disease classification based on computer analysis is evaluated.

An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images

“…In fact, CT texture analysis of the lungs has been applied to interstitial lung disease diagnosis 18 , emphysema quantification 16 , lung abnormality detection (e.g., honeycombing, ground glass, etc.) 44 , pulmonary thromboembolism characterization 45 , pulmonary fibrosis 22 , and COPD 21 .…”

“…The identification of robust imaging biomarkers associated with pulmonary function may help standardize diagnostic criteria for lung disease 14 . This relationship has been widely studied to better characterize relevant pulmonary conditions such as, emphysema 15,16 , asthma 17 , interstitial lung disease 18 , lung inflammation 19 , chronic obstructive pulmonary disease 20,21 , and pulmonary fibrosis 22 . A majority of these approaches are based on texture analysis and intensity thresholding.…”

Age-associated changes in aging lungs: A study with auto-segmentation and radiomics based on CT images

“…A stronger correlation coe cient was found for SRLGLE, suggesting that spatial information may be more useful than intensity information in the identi cation. In addition to texture features, radiomic analysis often requires the analysis of a combination of intensity, morphology, fractal geometry and higher-order features [33]. This information is integrated, and may thereby provide novel insights and a better detail of the lung using CT images.…”

Quantitative analysis of colorectal lesions observed on magnified endoscopy images

“…To estimate the textural properties of the pit patterns, a total of 14 different textural features were extracted from each ROI on the magnified images; that is, algorithms were used to obtain the following: (1) gray-level histogram moments (GLHM): mean, variance, skewness, kurtosis; (2) spatial gray-level dependent matrices (SGLDM): energy, entropy, correlation, local homogeneity, inertia; (3) gray-level difference matrices (GLDM): contrast, angular second moment, entropy, mean, inverse difference moment [15][16][17][22][23][24][25]. Table 2 lists the 14 different textural features.…”