3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS)

SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2017-11, Vol.36 (11), p.2239-2249
Main Authors: Farhangi, M. Mehdi, Frigui, Hichem, Seow, Albert, Amini, Amir A.
Format: Article
Language:English
Subjects:
Active contours
Adaptive shape prior
Algorithms
Cancer
Computed tomography
Databases, Factual
dictionary learning
Evolution
Humans
Image processing
Image reconstruction
Image segmentation
Imaging, Three-Dimensional - methods
Level set
level set segmentation
Lung - diagnostic imaging
Lung Neoplasms - diagnostic imaging
Lung nodules
Lungs
Nodules
Radiographic Image Interpretation, Computer-Assisted - methods
Representations
Shape
sparse representation
State of the art
Three-dimensional displays
Tomography, X-Ray Computed - methods
Training
Weight
X-ray CT
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Summary:SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. For the problem of lung nodule segmentation in X-ray CT, SCoTS offers a unified framework, capable of segmenting nodules of all types. Experimental validations are demonstrated on 542 3-D lung nodule images from the LIDC-IDRI database. Despite its generality, SCoTS is competitive with domain specific state of the art methods for lung nodule segmentation.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2017.2720119