Automatic liver segmentation in abdomen CT images using SLIC and adaboost algorithms
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computing Machinery
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study is an implementation of liver segmentation on abdomen CT images. The liver organ was segmented by using SLIC super-pixel and AdaBoost algorithms. Firstly, the images were clustered by SLIC super-pixel algorithm. Then, the liver was segmented by AdaBoost classifier. The segmentation process was done automatically. The automatic segmentation is based on the classification of overlapping patches of the image. The results of automatic segmentation and manual segmentation were compared and the efficiency of the method was observed. The best Dice rate was obtained as 92.13% and the best Jaccard rate was obtained as 85.8% on 16 abdomen CT images. © 2018 Association for Computing Machinery.
Açıklama
RIED, Tokai University
8th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2018 -- 18 January 2018 through 20 January 2018 -- 135712
8th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2018 -- 18 January 2018 through 20 January 2018 -- 135712
Anahtar Kelimeler
Automatic segmentation, Classification, Liver, SLIC super-pixel
Kaynak
ACM International Conference Proceeding Series
WoS Q Değeri
Scopus Q Değeri
N/A
