Hi Everyone,
I am looking for reference papers or reports about the impact of linear transformations of data before computing GLCM-derived statistics (haralick features).
More precisely, I am interested in analytical formulations of these Haralicks features when data are preprocessed by any linear transformation.
Thank you in advance for your help or your thought
Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90 % differentiation rate, the Haralick features achieved the highest AUC value of 92.70 % at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.
J Digit Imaging. 2015 Feb; 28(1): 99–115.
Published online 2014 Aug 13. doi: 10.1007/s10278-014-9718-8
Thank you Tarun. I am afraid I made a typo in formulating the title:
"Impact on GLCM-derived indexes (haralick features) when applied to linearly transformed data?"
Thank you again for your help on this