A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images

Abhinav K. Jha; Jeffrey J. Rodríguez; Renu M. Stephen; Alison T. Stopeck

doi:10.1109/SSIAI.2010.5483911

A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images

Abhinav K. Jha, Jeffrey J. Rodríguez, Renu M. Stephen, Alison T. Stopeck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

Abstract

In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

Original language	English (US)
Title of host publication	2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings
Pages	93-96
Number of pages	4
DOIs	https://doi.org/10.1109/SSIAI.2010.5483911
State	Published - 2010
Event	2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Austin, TX, United States Duration: May 23 2010 → May 25 2010

Publication series

Name	Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Other

Other	2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010
Country/Territory	United States
City	Austin, TX
Period	5/23/10 → 5/25/10

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computer Science Applications

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10.1109/SSIAI.2010.5483911

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Jha, A. K., Rodríguez, J. J., Stephen, R. M., & Stopeck, A. T. (2010). A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images. In 2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings (pp. 93-96). Article 5483911 (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation). https://doi.org/10.1109/SSIAI.2010.5483911

A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images. / Jha, Abhinav K.; Rodríguez, Jeffrey J.; Stephen, Renu M. et al.
2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings. 2010. p. 93-96 5483911 (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jha, AK, Rodríguez, JJ, Stephen, RM & Stopeck, AT 2010, A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images. in 2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings., 5483911, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 93-96, 2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010, Austin, TX, United States, 5/23/10. https://doi.org/10.1109/SSIAI.2010.5483911

Jha AK, Rodríguez JJ, Stephen RM, Stopeck AT. A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images. In 2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings. 2010. p. 93-96. 5483911. (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation). doi: 10.1109/SSIAI.2010.5483911

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title = "A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images",

abstract = "In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.",

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N2 - In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

AB - In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

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BT - 2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings

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Y2 - 23 May 2010 through 25 May 2010

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A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images

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