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1 April 2020 Active contours for multi-region segmentation with a convolutional neural network initialization
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Active contour-based methods are widely popular in the image segmentation field. Basically, they perform a semiautomatic region identification by partitioning the image content mainly into the foreground and background. Nevertheless, the accurate delimitation still remains as an important challenge which usually depends on how close the initial contour is placed to the object of interest (OI). Several applications of active contours require the user interaction to give prior information about the initial position as the first step, which drives the tool substantially dependent on a manual process. This paper describes how to overcome this limitation by including the expertise provided by the training stage of a Convolutional Neural Network (CNN). Despite CNN methods require a large dataset or data augmentation techniques to improve their results, the combined proposal accomplishes a presegmentation task with a reduced number of images to obtain the assumed locations for each OI. These results are used to initialize a multiphase active contour model that follows a level set scheme to lead a smoother multiregion segmentation with less effort. Experiments of this approach are included to compare classic techniques of contour initialization and show the benefits of our proposal.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erik Carbajal-Degante, Steve Avendaño, Leonardo Ledesma, Jimena Olveres, and Boris Escalante-Ramírez "Active contours for multi-region segmentation with a convolutional neural network initialization", Proc. SPIE 11353, Optics, Photonics and Digital Technologies for Imaging Applications VI, 1135307 (1 April 2020);

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