Segmentation of histopathological images with Convolutional Neural Networks using Fourier features
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052
Anahtar Kelimeler
Convolutional Neural Network; Fourier Transform; Histopathologic Images; Segmentation; Spatial Relations, Classification (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 Networks
Kaynak
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
