Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
BIOAXIS DNA RESEARCH CENTRE PRIVATE LIMITED
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Cell segmentation and counting has a very important role in diagnosing diseases and in the treatment process. But the complexity of the histopathological images and the differences in cell groups make this process very difficult, even for an expert. In order to facilitate this process, analysis of histopathological images is performed by using computer vision methods. This paper presents the use of different feature extraction methods for cell detection in histopathological images and the comparison of the results of these algorithms. For this reason, HOG, MSER, SIFT, FAST, LBP and CANNY feature extraction algorithms are used. The aim of the study is to determine cells using different feature extraction methods and to determine which of these feature extraction algorithms will be more successful. Firstly, image pre-processing has been applied to clear the noises in the histopathological images. Then, feature extraction algorithms are applied to image, respectively. Finally, the successes of different feature extraction algorithms have been compared.
Açıklama
Anahtar Kelimeler
HOG, MSER, SIFT, FAST, LBP, canny, histopathological image, cell counting
Kaynak
HELIX
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
8
Sayı
3
