Diffusion methods for aligning medical datasets: location prediction in CT scan images.
The purpose of this study is to introduce diffusion methods as a tool to label CT scan images according to their position in the human body. A comparative study of different methods based on a k-NN search is carried out and we propose a new, simple and efficient way of applying diffusion techniques that is able to give better location forecasts than methods that can be considered the current state-of-the-art.
Dpto. Ingeniería Informática, Universidad Autónoma de Madrid, 28049 Madrid, Spain. Electronic address: a.fernandez@uam.es.
Ángela
Á
Fernández
Afeka, Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel. Electronic address: netar@afeka.ac.il.
Neta
N
Rabin
Applied Mathematics Department, Yale University, New Haven, CT 06520, USA. Electronic address: coifman@math.yale.edu.
Ronald R
RR
Coifman
Imaging Department, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva, Israel. Electronic address: eksteinj@clalit.org.il.
Joseph
J
Eckstein
D000465:Algorithms; D016880:Anisotropy; D004058:Diffusion; D006801:Humans; D056888:Patient Positioning; D025341:Principal Component Analysis; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*
Anisotropic diffusion; CT scan images; Diffusion distance; Diffusion methods; Misaligned data