Adaptive bias field correction: Application on abdominal MR images

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Abstract

Segmentation of medical images is one of the most important phases for disease diagnosis. Accuracy, robustness and stability of the results obtained by image segmentation is a major concern. Many segmentation methods rely on absolute values of intensity level, which are affected by a bias term due to in-homogeneous field in magnetic resonance images. The main objective of this paper is two folded: (1) To show efficiency of an energy minimization based approach, which uses intrinsic component optimization, on abdominal magnetic resonance images. (2) To propose an adaptive method to stop the optimization automatically. The proposed method can control the value of the energy functional and stops the iteration efficiently. Comparisons with two previous state-of-the art methods indicate a better performance of the proposed method.

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Goceri, E., Dura, E., Esteve, J. D., & Gunay, M. (2018). Adaptive bias field correction: Application on abdominal MR images. Lecture Notes in Computational Vision and Biomechanics, 27, 102–110. https://doi.org/10.1007/978-3-319-68195-5_11

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