Abstract
In this paper, we propose a method for semi-supervised image segmentation based on geometric active contours. The main novelty of the proposed method is the initialization of the segmentation process, which is performed with a polynomial approximation of a user defined initialization (for instance, a set of points or a curve to be interpolated). This work is related to many potential applications: the geometric conditions can be useful to improve the quality the segmentation process in medicine and geophysics when it is required (weak contrast of the image, missing parts in the image, non-continuous contour…). We compare our method to other segmentation algorithms, and we give experimental results related to several medical and geophysical applications.
Highlights
C.; Chaumont-Frelet, T.; Kuksenko, S.The problem of segmenting an image into its significant components has been studied for over 30 years in computers science, applied mathematics and more generally in computer vision.Recently, from convolutional neural networks to recurrent neural networks (Hochreiter and Schmidhuber [4]), encoder–decoders (Badrinarayanan et al [5]), or generative adversarial networks (Goodfellow et al [6]), deep learning based techniques have achieved huge successes in the field of artificial intelligence and image segmentation
In general, the performance heavily depends on labeled data, and this point is a main difficulty on several applications when lacking labeled data such as in many medical and geophysical applications
The efficiency of the method has been shown on 2D images, knowing that such images are difficult to segment with usual approaches since the searched contour is not continuous or when the zone to segment is blurred
Summary
C.; Chaumont-Frelet, T.; Kuksenko, S.The problem of segmenting an image into its significant components has been studied for over 30 years in computers science, applied mathematics and more generally in computer vision.Recently, from convolutional neural networks (see Fukushima [1], Waibel et al [2], andLeCun et al [3], among others. . . ) to recurrent neural networks (Hochreiter and Schmidhuber [4]), encoder–decoders (Badrinarayanan et al [5]), or generative adversarial networks (Goodfellow et al [6]), deep learning based techniques have achieved huge successes in the field of artificial intelligence and image segmentation (see [7] for a recent survey). The problem of segmenting an image into its significant components has been studied for over 30 years in computers science, applied mathematics and more generally in computer vision. ) to recurrent neural networks (Hochreiter and Schmidhuber [4]), encoder–decoders (Badrinarayanan et al [5]), or generative adversarial networks (Goodfellow et al [6]), deep learning based techniques have achieved huge successes in the field of artificial intelligence and image segmentation (see [7] for a recent survey). These approaches leads to excellent results, including in medical applications The data augmentation is possible [9], but it is often complicated, and requires more CPU/GPU time than other segmentation techniques based on energy minimization or variational approaches (Kass et al [10], Chand and Vese [11], Mumford and Shah [12], Vese and Le Guyader [13]. . . ) or geometrical ones (fast marching methods, see Sethian [14] or Forcadel et al [15])
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