Title :
Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model
Author :
Zarpalas, Dimitrios ; Gkontra, Polyxeni ; Daras, Petros ; Maglaveras, Nicos
Author_Institution :
Centre for Res. & Technol. Hellas, Inf. Technol. Inst., Thessaloniki, Greece
Abstract :
Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method.
Keywords :
biomedical MRI; brain; image segmentation; medical disorders; medical image processing; optimisation; accurate automatic hippocampus segmentation; brain disorder diagnosis; brain disorder follow-up; brain disorder prevention; data sets; extended multiatlas concept; fully automatic hippocampus segmentation; hybrid active contour model; magnetic resonance imaging; multiatlas concept; optimization scheme; structural integrity; subject-specific 3D optimal local maps; training images; Active appearance model; Active contours; Algorithm design and analysis; Biomedical imaging; Hippocampus; Image edge detection; Image segmentation; Solid modeling; Hippocampus segmentation; hybrid active contour model (ACM); local weighting scheme; multi-atlas; optimal local maps (OLMs); prior knowledge;
Journal_Title :
Translational Engineering in Health and Medicine, IEEE Journal of
DOI :
10.1109/JTEHM.2014.2297953