Hatipolu, NuhBilgin, Gokhan2024-06-122024-06-122015978-1-4673-7386-92165-0608https://hdl.handle.net/20.500.14551/2160523nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYThe study aims to boost the success of the segmentation results by evaluating spatial relations in the segmentation of histopathalogical images. In the first step Fourier features are extracted from RGB color space of digital histopathalogical images. Training data sets are formed by selecting equal number of different cellular and extra-cellular structures in spatial domain from the images. Classification models of each training data set is obtained by utilizing Convolutional Neural Network (CNN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) methods. Visual and numerical outputs which are obtained from supervised training methods are presented for comparison purpose in the experimental results section.trinfo:eu-repo/semantics/closedAccessHistopathologic ImagesConvolutional Neural NetworkSegmentationFourier TransformSpatial RelationsConference Object455458N/AWOS:000380500900092