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Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

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Abstract

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Central University, Chungli, 320, Taiwan

    Kuo-Kai Shyu, Van-Truong Pham, Thi-Thao Tran & Po-Lei Lee

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  1. Kuo-Kai Shyu
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  2. Van-Truong Pham
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  3. Thi-Thao Tran
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  4. Po-Lei Lee
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Correspondence to Kuo-Kai Shyu.

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Shyu, KK., Pham, VT., Tran, TT. et al. Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation. Machine Vision and Applications 23, 1159–1175 (2012). https://doi.org/10.1007/s00138-011-0373-5

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  • DOI: https://doi.org/10.1007/s00138-011-0373-5

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