Integrated spatio-temporal noise reduction with implicit motion compensation

Author(s):  
O.A. Ojo ◽  
T.G. Kwaaitaal-Spassova
2014 ◽  
Vol E97.D (2) ◽  
pp. 380-383 ◽  
Author(s):  
Sangwoo AHN ◽  
Jongjoo PARK ◽  
Linbo LUO ◽  
Jongwha CHONG

2021 ◽  
Vol 237 ◽  
pp. 109544
Author(s):  
Gustavo E. Coelho ◽  
Maria Graça Neves ◽  
António Pascoal ◽  
Álvaro Ribeiro ◽  
Peter Frigaard

2014 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Guillaume Noyel ◽  
Jesus Angulo ◽  
Dominique Jeulin ◽  
Daniel Balvay ◽  
Charles-André Cuenod

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.


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