A computer-implemented method for determining striation properties of fatigue striations on a sample surface of a part , the method comprising: 1.1 with a scanning microscope device , imaging the sample surface so as to obtain a sample image potentially containing the fatigue striations ; 1.2 with an image patch selecting device, selecting a sample image patch potentially containing the fatigue striations from the sample image for further processing; 1.3 with a pre-processing device, normalizing the sample image patch by performing any of the following operations on the sample image patch : - a Gaussian filter operation; and/or - a histogram equalization; and/or - an image binarization and subsequent morphological operation, such as erosion, dilation or skeleton-operations; and/or - a frequency domain self-filtering operation; and/or - a window operation; and/or - a normalization operation to normalize intensity values to a fixed range; and enhancing line-like regular structures contained in the sample image patch so as to obtain a normalized image patch ; 1.4 with an autocorrelation device, determining the autocorrelation of the normalized image patch so as to obtain an autocorrelation image patch ; 1.5 with a Radon transform device, performing a Radon transform of the autocorrelation image patch so as to obtain a transformed image patch ; 1.6 with a frequency analysis device, performing a spectral analysis of the transformed image patch and determining a power spectrum of the transformed image patch ; 1.7 with a striation property determining device, determining, from the power spectrum , at least one striation property that is suitable to indicate the presence of fatigue striations in the sample image patch . 1.1 with a scanning microscope device , imaging the sample surface so as to obtain a sample image potentially containing the fatigue striations ; 1.2 with an image patch selecting device, selecting a sample image patch potentially containing the fatigue striations from the sample image for further processing; 1.3 with a pre-processing device, normalizing the sample image patch by performing any of the following operations on the sample image patch : - a Gaussian filter operation; and/or - a histogram equalization; and/or - an image binarization and subsequent morphological operation, such as erosion, dilation or skeleton-operations; and/or - a frequency domain self-filtering operation; and/or - a window operation; and/or - a normalization operation to normalize intensity values to a fixed range; and enhancing line-like regular structures contained in the sample image patch so as to obtain a normalized image patch ; - a Gaussian filter operation; and/or - a histogram equalization; and/or - an image binarization and subsequent morphological operation, such as erosion, dilation or skeleton-operations; and/or - a frequency domain self-filtering operation; and/or - a window operation; and/or - a normalization operation to normalize intensity values to a fixed range; 1.4 with an autocorrelation device, determining the autocorrelation of the normalized image patch so as to obtain an autocorrelation image patch ; 1.5 with a Radon transform device, performing a Radon transform of the autocorrelation image patch so as to obtain a transformed image patch ; 1.6 with a frequency analysis device, performing a spectral analysis of the transformed image patch and determining a power spectrum of the transformed image patch ; 1.7 with a striation property determining device, determining, from the power spectrum , at least one striation property that is suitable to indicate the presence of fatigue striations in the sample image patch . <EOS>