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A deformable model for automatic CT liver extraction.

Jean Gao·Akio Kosaka·Avinash Kak
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Abstract

RATIONALE AND OBJECTIVES: This study was performed to design an automatic liver region extraction system to facilitate clinical liver size estimation and further serve as a prestage for liver reconstruction and volume estimation. MATERIALS AND METHODS: We present a modification of the well-known snakes algorithm for extracting liver regions in noisy CT images. Our modification addresses the issues of selection of the control points on an estimate of the contour and the determination of the weighting coefficients. The weighting coefficients are determined dynamically on the basis of the distance between the control points and the local curvature of the contour. RESULTS: The proposed method was used in extracting liver regions from 98 cross-sectional abdominal images. The overall performance was estimated by comparisons with original liver regions. CONCLUSION: The deformable model method enables an efficient and effective automatic liver region extraction in noisy environments. This approach eliminates human-in-the loop, which is the common practice for the majority of current methods.

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AlgorithmsHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedImaging, Three-DimensionalLiverTomography, Spiral ComputedJournal Article

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BibTeXRIS

Jean Gao, Akio Kosaka, Avinash Kak. 2005. A deformable model for automatic CT liver extraction.. https://doi.org/10.1016/j.acra.2005.05.005

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