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Med Image Anal. 2017 Apr;37:46-55. doi: 10.1016/j.media.2017.01.002. Epub 2017 Jan 13.

Adaptive local window for level set segmentation of CT and MRI liver lesions.

Author information

1
Departments of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA. Electronic address: ahoogi@stanford.edu.
2
Department of Radiology and, by courtesy, Orthopedic Surgery, Stanford University, Stanford, CA, USA. Electronic address: beaulieu@stanford.edu.
3
Department of Radiology, University of California, San Diego Medical Center, San Diego, CA, USA. Electronic address: mouracunha@hotmail.com.
4
Department of Radiology, University of California, San Diego Medical Center, San Diego, CA, USA. Electronic address: erheba@ucsd.edu.
5
Department of Radiology, University of California, San Diego Medical Center, San Diego, CA, USA. Electronic address: csirlin@ucsd.edu.
6
Department of Radiology and, by courtesy, Electrical Engineering and Medicine, Stanford University, Stanford, CA, USA. Electronic address: snapel@stanford.edu.
7
Departments of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA. Electronic address: dlrubin@stanford.edu.

Abstract

We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).

KEYWORDS:

Adaptive local window; Deformable models; Lesion segmentation

PMID:
28157660
PMCID:
PMC5393306
DOI:
10.1016/j.media.2017.01.002
[Indexed for MEDLINE]
Free PMC Article

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