Clustering of the human skeletal muscle fibers using linear programming and angular Hilbertian metrics.
In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.
Neji
Radhouène
R
Laboratoire MAS, Ecole Centrale Paris, Châtenay-Malabry, France.
Besbes
Ahmed
A
Komodakis
Nikos
N
Deux
Jean-François
JF
Maatouk
Mezri
M
Rahmouni
Alain
A
Bassez
Guillaume
G
Fleury
Gilles
G
Paragios
Nikos
N
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018485:Muscle Fibers, Skeletal / Q000473:pathology*; D009135:Muscular Diseases / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011382:Programming, Linear; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity