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Adaptive fast marching method for automatic liver segmentation from CT images.

Xiao Song¹, Ming Cheng, Boliang Wang

¹College of Computer and Information Technology, Nanyang Normal University, Nanyang 473061, China.

Medical Physics

|September 7, 2013

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This study introduces an automatic method for liver segmentation using adaptive fast marching methods (FMM). The approach achieves high accuracy and efficiency in segmenting liver regions on CT scans.

Area of Science:

Medical Imaging
Computational Anatomy
Image Processing

Background:

Accurate liver segmentation is crucial for computer-aided diagnosis and surgical planning.
Existing methods often require manual intervention or lack efficiency.

Purpose of the Study:

To develop an automatic seed point selection and adaptive fast marching method (FMM) for efficient and accurate liver segmentation.
To improve upon existing liver segmentation techniques for clinical applications.

Main Methods:

An automatic seed point selection method based on liver structure and intensity characteristics.
An adaptive FMM with self-adaptive parameter adjustment for liver segmentation on CT slices.
Preprocessing steps include thresholding, smoothing, and nonlinear grayscale conversion to enhance liver visibility.

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Main Results:

Validation on 30 abdominal CT datasets.
Achieved a 98.7% true positive rate and a 96.7% DICE coefficient.
Processing time is approximately 0.30 seconds per 512x512 pixel slice.

Conclusions:

The proposed method enables fast and accurate liver segmentation.
Demonstrates successful application in clinical settings for liver imaging analysis.