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Interactive binary active contours for prostate contour delineation
2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012We present a new interactive segmentation framework to delineate the prostate from MR images. We first explicitly address the segmentation problem based on fast globally Finsler Active Contours (FAC) by incorporating both statistical and geometric shape prior knowledge.
Foued Derraz +3 more
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Contour tracking via on-line discriminative active contours
2014 IEEE International Conference on Image Processing (ICIP), 2014This paper presents a novel on-line AdaBoost based discriminative active contour tracking framework (ADACT) using level sets. First we build an on-line AdaBoost based appearance model to track and extract the rough target region, which provides important discriminative clues for our active contour model.
Peng Lv, Qingjie Zhao, Dongbing Gu
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Contour extraction by multi-level active contour model
6th International Conference on Signal Processing, 2002., 2003In this paper, an innovative algorithm for object segmentation and contour extraction is proposed, where the active contour evolution based on the Mumford-Shah model is performed on a coarse-to-fine approach spanned by wavelet transform. The multi-level active contour model consists of three main parts: 1).
null Yang Li, null Yang Xin, null He Yi
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2012
We introduce an algorithm for active contour segmentation in which the level set function encoding the contour is processed by median filtering using morphological amoebas. These are adaptive structure elements introduced by Lerallut et al. which can be combined with different morphological operations.
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We introduce an algorithm for active contour segmentation in which the level set function encoding the contour is processed by median filtering using morphological amoebas. These are adaptive structure elements introduced by Lerallut et al. which can be combined with different morphological operations.
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Active Contours and Active Surfaces
2012Active contours and active surfaces are means of model-driven segmentation. Their use enforces closed and smooth boundaries for each segmentation irrespective of the image content. They are particularly useful if such properties cannot be derived everywhere from the data.
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