Hatipo?lu N.Bilgin G.2024-06-122024-06-1220159.78147E+12https://doi.org/10.1109/SIU.2015.7129857https://hdl.handle.net/20.500.14551/163322015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052The 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. © 2015 IEEE.tr10.1109/SIU.2015.7129857info:eu-repo/semantics/closedAccessConvolutional Neural Network; Fourier Transform; Histopathologic Images; Segmentation; Spatial RelationsClassification (Of Information); Convolution; Fourier Transforms; Image Segmentation; Nearest Neighbor Search; Numerical Methods; Support Vector Machines; Classification Models; Histopathologic Images; Histopathological Images; K-Nearest Neighbors; Segmentation Results; Spatial Relations; Supervised Trainings; Training Data Sets; Convolutional Neural NetworksConference Object4554582-s2.0-84939138652N/A