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Objective: The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods: We propose a segmentation-guided partially-joint regression forest (S-PRF) model to automatically...
Image segmentation is a key point for analyzing and understanding image, which occupies an important position in image processing. Recent studies have attempted to tackle pixel level labeling tasks using deep learning. In our paper, we propose an approach of combining fully convolutional network and conditional random field for image semantic segmentation. We utilize FCN model to automatically learn...
In this paper, a new region-based active contour model is proposed for image segmentation. Different from the general region-based active contour models, this model partitions the regions of interests in images depending on the local statistics of the intensity and the magnitude of gradient in the neighborhood of the contour. Inspired by the structure tensor method, an improved regularization term...
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