Open AccessA variational joint segmentation and registration framework for multimodal images
Author(s) -
Adela Ademaj,
Lavdie Rada,
Mazlinda Ibrahim,
Ke Chen
Publication year - 2020
Publication title -
journal of algorithms and computational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.234
H-Index - 13
eISSN - 1748-3026
pISSN - 1748-3018
DOI - 10.1177/1748302620966691
Subject(s) - artificial intelligence , segmentation , computer science , computer vision , smoothing , image segmentation , scale space segmentation , image registration , mutual information , joint (building) , segmentation based object categorization , object (grammar) , image (mathematics) , pattern recognition (psychology) , architectural engineering , engineering
Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.