FENS Seminars – M. Buket Darıcı

Abstract: Medical image registration is an important preprocess of image-guided systems. Since image registration brings the images to the same coordinate system of the specified reference image, image registration should not be neglected to be able to make accurate comparisons between results obtained from medical images. Basically, registration is an optimization problem. The parameters of the specified transformation algorithm are optimized based on specified functions and parameters of registration. In this study, T1-weighted structural 3D brain MR images on IXI dataset have been registered into reference image by the affine transformation in the proposed registration method. During experiments, the effects of several parameters and functions on registration performance have been investigated with different preprocessing techniques applied to brain MR images. Additionally, the other vital preprocessing steps such as Bias Field Correction, and Skull Stripping have been highlighted in this study. After several experiments, the most successful outcome of various experiments was achieved by using Powell optimization function along with Linear Interpolation, when applying Median Filter with CLAHE to images in the suggested registration method. The NCC was used to compare the registration results. The study’s results demonstrate that the proposed registration method outperformed the widely-used registration tool SPM8 with mean NCC of -0.753.

About the Speaker: Muazzez Buket Darıcı received her BS degree in Electrical-Electronics Engineering at Kadir Has University in 2018, then she received her MS degree in Electronics Engineering at İstanbul Technical University in 2020. Her primary research interests are Artificial Intelligence in Medicine, Disease Classification, Image Processing, and Deep Learning. She works as a Research Assistant with the Department of Electrical-Electronics Engineering, and she is currently a Ph.D student at Kadir Has University.