Dominant Orientation using Fourier Power Spectrum I have been using Fourier Transform Power Spectrum for estimating the dominant direction in some images. 

However, I am now working on extremely small images of size 8X8 pixel and I have found that even at this level there is some result regarding the dominant orientation of the image. As the image size is 8X8, the total number of pixel will be 64 and as shown in the figure below, there will be less number of angles. 

I have simply done polar conversion  of the Fourier Transform Power Spectrum of the image which will be of same size (8X8), then I have taken the summation of the intensity for each angle and plotted them against the angle. 



Now, from the plot I can find the maximum angle but I want to increase the accuracy of my result. I will first find the dominant direction of an image (a medical image) and then I will find the changes in the diseased state.

Thank you so much for your help.
Subtract from the 8x8 image the mean value of its pixels, which should not affect the dominant orientation. This will remove artifacts due to windowing in the next step.
Window the image with a circularly symmetric window function that eliminates artifacts aligned with the image axes. Make sure you have chosen a window function that is sufficiently smooth to be sampled with a 8x8 grid without frequency domain aliasing.
Pad the image to 32x32 with zeros to increase the image size and thus the discrete Fourier transform (DFT) resolution. This makes the DFT result smoother and thus easier to interpolate.
Calculate for each angle an integral of the squared magnitude of the DFT result over a line corresponding to the angle. All angles must be treated equally; use same length lines. 4x4 piece-wise bicubic polynomial interpolation should suffice if you approximate the integral by a sum of point samples at the axis-aligned resolution of the DFT.


If you want to make the computation faster you can take advantage of that the integral will change quite smoothly as function of the angle, so you can first sample at the coarse resolution of at least $\text{atan}(1/8)\times 360\unicode{xb0}/(2\pi) \approx 7\unicode{xb0}$ to find where the highest peak might be, and then sample more finely around that angle.