An algorithm to stabilize a sequence of thermal brain images

Complex challenges of optical imaging in diagnostics and surgical treatment require accurate image registration/stabilization methods that remove only unwanted motions. An SIAROI algorithm is proposed for real-time subpixel registration sequences of intraoperatively acquired infrared (thermal) brain images. SIAROI algorithm is based upon automatic, localized Subpixel Image Autocorrelation and a user-selected Region of Interest (ROI). Human expertise about unwanted motions is added through a user-outlined ROI, using a low-accuracy free-hand paintbrush. SIAROI includes: (a) propagating the user-outlined ROI by selecting pixels in the second image of the sequence, using the same ROI; (b) producing SROI (sub-pixel ROI) by converting each pixel to k=N×N subpixels; (c) producing new SROI in the second image by shifting SROI within plus or minus 6k subpixels; (d) finding an optimal autocorrelation shift (x,y) within 12N that minimizes the Standard Deviation of Differences of Pixel Intensities (SDDPI) between corresponding ROI pixels in both images, (e) shifting the second image by (x,y), repeating (a)-(e) for successive images (t,t+1). In experiments, a user quickly outlined non-deformable ROI (such as bone) in the first image of a sequence. Alignment of 100 brain images (25600×25600 pixel search, after every pixel was converted to 100 sub-pixels), took ~3 minutes, which is 200 times faster (with a 0.1=ROI/image ratio) than global auto-correlation. SIAROI improved frame alignment by a factor of two, relative to a Global Auto-correlation and Tie-points-based registration methods, as measured by reductions in the SDDPI.