TY - GEN
T1 - Patient-specific manifold embedding of multispectral images using kernel combinations
AU - Zimmer, Veronika A.M.
AU - Fonolla, Roger
AU - Lekadir, Karim
AU - Piella, Gemma
AU - Hoogendoorn, Corné
AU - Frangi, Alejandro F.
PY - 2013
Y1 - 2013
N2 - This paper presents a framework that optimizes kernel-based manifold embedding for the characterization of multispectral image data. The hypothesis is that data manifolds corresponding to high-dimensional images can have varying characteristics and types of nonlinearity. As a result, kernel functions must be selected from a wide range of transformations and tuned on an image- and patient-basis. To this end, we introduce a new measure to assess the quality of the kernel transformations that takes into account both local and global relationships in nonlinear manifolds. Furthermore, the calculated measures for each kernel are used to combine the different kernel transformations further highlight the tissue constituents in all regions of the image. Validation with phantom and real multispectral image data shows improvement in the visualization and characterization of the tissue constituents.
AB - This paper presents a framework that optimizes kernel-based manifold embedding for the characterization of multispectral image data. The hypothesis is that data manifolds corresponding to high-dimensional images can have varying characteristics and types of nonlinearity. As a result, kernel functions must be selected from a wide range of transformations and tuned on an image- and patient-basis. To this end, we introduce a new measure to assess the quality of the kernel transformations that takes into account both local and global relationships in nonlinear manifolds. Furthermore, the calculated measures for each kernel are used to combine the different kernel transformations further highlight the tissue constituents in all regions of the image. Validation with phantom and real multispectral image data shows improvement in the visualization and characterization of the tissue constituents.
UR - http://www.scopus.com/inward/record.url?scp=84886738976&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02267-3_11
DO - 10.1007/978-3-319-02267-3_11
M3 - Conference contribution
AN - SCOPUS:84886738976
SN - 9783319022666
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 82
EP - 89
BT - Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PB - Springer-Verlag Italia
T2 - 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -